Method and apparatus for increasing performance of communication paths for communication nodes

ABSTRACT

A system that incorporates aspects of the subject disclosure may perform operations including, for example, obtaining performance measurements, identifying a performance measurement from the performance measurements that is below performance threshold and, in turn, initiating corrective action to improve the performance measurement of an affected network element falling below the performance threshold. The performance measurements can be determined from measurements associated with signals generated by communication devices, noise levels in a spectral portion used by communication devices to transmit the signals, and interference signals exceeding an adaptive inter-cell interference threshold. Other embodiments are disclosed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/703,041, filed May 4, 2015, which claims the benefit of U.S.Provisional Application No. 62/091,033 filed on Dec. 12, 2014, U.S.Provisional Application No. 62/010,305 filed on Jun. 10, 2014, and U.S.Provisional Application No. 61/988,712 filed on May 5, 2014. Allsections of the aforementioned application(s) and patent(s) areincorporated herein by reference in their entirety.

FIELD OF THE DISCLOSURE

The subject disclosure is related to a method and apparatus forincreasing performance of communication paths for communication nodes.

BACKGROUND OF THE DISCLOSURE

In most communication environments involving short range or long rangewireless communications, interference from unexpected wireless sourcescan impact the performance of a communication system leading to lowerthroughput, dropped calls, reduced bandwidth which can cause trafficcongestion, or other adverse effects, which are undesirable.

Some service providers of wireless communication systems have addressedinterference issues by adding more communication nodes, policinginterferers, or utilizing antenna steering techniques to avoidinterferers.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 depicts an illustrative embodiment of a communication system;

FIG. 2 depicts an illustrative embodiment of a frequency spectrum of afour carrier CDMA signal;

FIG. 3 depicts an illustrative embodiment of a frequency spectrum of afour carrier CDMA signal showing unequal power balancing between thefour CDMA carriers and including an interferer;

FIG. 4 depicts an illustrative embodiment of a base station of FIG. 1;

FIG. 5 depicts an illustrative embodiment of a frequency spectrum of afour carrier CDMA signal having four CDMA carriers with suppression ofan interferer that results in falsing;

FIG. 6 depicts an illustrative embodiment of an interference detectionand mitigation system;

FIG. 7 depicts an illustrative embodiment of an interference detectionand mitigation system;

FIG. 8 depicts an illustrative embodiment of signal processing module ofFIG. 7;

FIG. 9 depicts an illustrative embodiment of plots of a spread spectrumsignal;

FIG. 10 depicts an illustrative embodiment of a method for interferencedetection;

FIG. 11 depicts illustrative embodiments of the method of FIG. 10;

FIG. 12 depicts illustrative embodiments of a series of spread spectrumsignals intermixed with an interference signal;

FIG. 13 depicts an illustrative embodiment of a graph depictinginterference detection efficiency of a system of the subject disclosure;

FIG. 14 depicts illustrative embodiments of Long Term Evolution (LTE)time and frequency signal plots;

FIG. 15 depicts illustrative embodiments of LTE time and frequencysignal plots intermixed with interference signals;

FIG. 16 depicts an illustrative embodiment of a method for detecting andmitigating interference signals shown in FIG. 15;

FIG. 17 depicts an illustrative embodiment of adaptive thresholds usedfor detecting and mitigating interference signals shown in FIG. 15;

FIG. 18 depicts an illustrative embodiment of resulting LTE signalsafter mitigating interference according to the method of FIG. 16;

FIG. 19 depicts an illustrative embodiment of a method for mitigatinginterference;

FIG. 20 depicts an illustrative embodiment of a network design;

FIG. 21 depicts an illustrative embodiment of an Open SystemsInterconnect (OSI) model;

FIG. 22 depicts an illustrative embodiment of a relationship betweenSINR and data throughput and performance;

FIG. 23 depicts an illustrative embodiment of a closed loop process;

FIG. 24 depicts an illustrative embodiment of a spectral environment ofa wireless channel;

FIG. 25 depicts an illustrative embodiment of examples of spectralenvironments for various frequency bands;

FIG. 26A depicts an illustrative embodiment of a method for linkmanagement in a communication system;

FIG. 26B depicts an illustrative embodiment of a centralized systemmanaging cell sites according to aspects of the subject disclosure;

FIG. 26C depicts an illustrative embodiment of independently operatingcell sites according to aspects of the subject disclosure;

FIG. 26D depicts an illustrative embodiment of cell sites cooperatingwith each other according to aspects of the subject disclosure;

FIG. 27A depicts an illustrative embodiment of a method for determiningan adaptive inter-cell interference threshold based on thermal noisemeasured from unused paths;

FIG. 27B depicts an illustrative embodiment of another method 950 fordetermining an adaptive inter-cell interference threshold based on anestimated thermal noise energy;

FIG. 28 depicts an illustrative embodiment of a communication devicethat can utilize in whole or in part embodiments of the subjectdisclosure for detecting and mitigating interference; and

FIG. 29 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions, when executed, maycause the machine to perform any one or more of the methods describedherein.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrativeembodiments for detecting and mitigating interference signals. Otherembodiments are included in the subject disclosure.

One embodiment of the subject disclosure includes a system having amemory to store instructions, and a processor coupled to the memory.Upon execution of the instructions by the processor, the processor canperform operations including measuring signals generated bycommunication devices directed to the cell site and measuring noiselevels for a plurality of paths. The processor can also performoperations including measuring interference signals for the plurality ofpaths according to the adaptive inter-cell interference threshold and,in turn, determining Signal to Interference plus Noise Ratio (SINR)measurements for the plurality of paths according to the signals, thenoise levels and the interference signals measured for the plurality ofpaths. The processor can further perform operations includingidentifying a SINR measurement from the SINR measurements that is belowSINR threshold and, in turn, initiating a corrective action to improvethe SINR measurement of an affected path of the plurality of pathsfalling below the SINR threshold.

One embodiment of the subject disclosure includes a machine-readablestorage medium, comprising instructions, which when executed by aprocessor, can cause the processor to perform operations includingobtaining a resource block schedule for each of a plurality of pathsand, in turn, identifying resource blocks in the plurality of paths thatare not in use. The instructions can also cause the processor to performoperations including measuring for each of the plurality of paths anenergy of the resource blocks not in use to determine an average thermalnoise level for each path and, in turn, determining for each of theplurality of paths an adaptive inter-cell interference thresholdaccording to the average thermal noise level of each path. Theinstructions can further cause the processor to perform operationsincluding measuring interference signals according to the adaptiveinter-cell interference threshold for each of the plurality of paths,measuring signals and noise levels for each of the plurality of paths,and, in turn, determining Signal to Interference plus Noise Ratio (SINR)measurements for the plurality of paths according to the signals, thenoise levels and the interference signals measured for the plurality ofpaths. The instructions can cause the processor to perform operationsincluding identifying a SINR measurement that is below a SINR thresholdand, in turn, initiating a corrective action to improve the SINRmeasurement of an affected path of the plurality of paths falling belowthe SINR threshold.

One embodiment of the subject disclosure includes a method, performed bya system comprising a processor, including obtaining performancemeasurements. The performance measurements can be determined frommeasurements associated with signals generated by communication devices,noise levels in a spectral portion used by communication devices totransmit the signals, and interference signals exceeding an adaptiveinter-cell interference threshold. The method can also includeidentifying a performance measurement from the performance measurementsthat is below performance threshold and, in turn, initiating correctiveaction to improve the performance measurement of an affected networkelement falling below the performance threshold.

As shown in FIG. 1, an exemplary telecommunication system 10 may includemobile units 12, 13A, 13B, 13C, and 13D, a number of base stations, twoof which are shown in FIG. 1 at reference numerals 14 and 16, and aswitching station 18 to which each of the base stations 14, 16 may beinterfaced. The base stations 14, 16 and the switching station 18 may becollectively referred to as network infrastructure.

During operation, the mobile units 12, 13A, 13B, 13C, and 13D exchangevoice, data or other information with one of the base stations 14, 16,each of which is connected to a conventional land line communicationnetwork. For example, information, such as voice information,transferred from the mobile unit 12 to one of the base stations 14, 16is coupled from the base station to the communication network to therebyconnect the mobile unit 12 with, for example, a land line telephone sothat the land line telephone may receive the voice information.Conversely, information, such as voice information may be transferredfrom a land line communication network to one of the base stations 14,16, which in turn transfers the information to the mobile unit 12.

The mobile units 12, 13A, 13B, 13C, and 13D and the base stations 14, 16may exchange information in either narrow band or wide band format. Forthe purposes of this description, it is assumed that the mobile unit 12is a narrowband unit and that the mobile units 13A, 13B, 13C, and 13Dare wideband units. Additionally, it is assumed that the base station 14is a narrowband base station that communicates with the mobile unit 12and that the base station 16 is a wideband digital base station thatcommunicates with the mobile units 13A, 13B, 13C, and 13D.

Narrow band format communication takes place using, for example,narrowband 200 kilohertz (KHz) channels. The Global system for mobilephone systems (GSM) is one example of a narrow band communication systemin which the mobile unit 12 communicates with the base station 14 usingnarrowband channels. Alternatively, the mobile units 13A, 13B, 13C, and13D communicate with the base stations 16 using a form of digitalcommunications such as, for example, code-division multiple access(CDMA), Universal Mobile Telecommunications System (UMTS), 3GPP LongTerm Evolution (LTE), or other next generation wireless accesstechnologies. CDMA digital communication, for instance, takes placeusing spread spectrum techniques that broadcast signals having widebandwidths, such as, for example, 1.2288 megahertz (MHz) bandwidths.

The switching station 18 is generally responsible for coordinating theactivities of the base stations 14, 16 to ensure that the mobile units12, 13A, 13B, 13C, and 13D are constantly in communication with the basestation 14, 16 or with some other base stations that are geographicallydispersed. For example, the switching station 18 may coordinatecommunication handoffs of the mobile unit 12 between the base stations14 and another base station as the mobile unit 12 roams betweengeographical areas that are covered by the two base stations.

One particular problem that may arise in the telecommunication system 10is when the mobile unit 12 or the base station 14, each of whichcommunicates using narrowband channels, interferes with the ability ofthe base station 16 to receive and process wideband digital signals fromthe digital mobile units 13A, 13B, 13C, and 13D. In such a situation,the narrowband signal transmitted from the mobile unit 12 or the basestation 14 may interfere with the ability of the base station 16 toproperly receive wideband communication signals.

As will be readily appreciated, the base station 16 may receive andprocess wideband digital signals from more than one of the digitalmobile units 13A, 13B, 13C, and 13D. For example, the base station 16may be adapted to receive and process four CDMA carriers 40A-40D thatfall within a multi-carrier CDMA signal 40, as shown in FIG. 2. In sucha situation, narrowband signals transmitted from more than one mobileunits, such as, the mobile unit 12, may interfere with the ability ofthe base station 16 to properly receive wideband communication signalson any of the four CDMA carriers 40A-40D. For example, FIG. 3 shows amulti-carrier CDMA signal 42 containing four CDMA carriers 42A, 42B, 42Cand 42D adjacent to each other wherein one of the CDMA carriers 42C hasa narrowband interferer 46 therein. As shown in FIG. 3, it is quiteoften the case that the signal strengths of the CDMA carrier signals42A-42D are not equal.

As disclosed in detail hereinafter, a system and/or a method formultiple channel adaptive filtering or interference suppression may beused in a communication system. In particular, such a system or methodmay be employed in a communication system to protect against, or toreport the presence of, interference, which has deleterious effects onthe performance of the communication system. Additionally, such a systemand method may be operated to eliminate interference in CDMA carriershaving other CDMA carriers adjacent thereto.

The foregoing system and methods can also be applied to other protocolssuch as AMPS, GSM, UMTS, LTE, VoLTE, 802.11xx, 5G, next generationwireless protocols, and so on. Additionally, the terms narrowband andwideband referred to above can be replaced with sub-bands, concatenatedbands, bands between carrier frequencies (carrier aggregation), and soon, without departing from the scope of the subject disclosure. It isfurther noted that the term interference can represent emissions withinband (narrowband or wideband), out-of-band interferers, interferencesources outside cellular (e.g., TV stations, commercial radio or publicsafety radio), interference signals from other carriers (inter-carrierinterference), interference signals from user equipment (UEs) operatingin adjacent base stations, and so on. Interference can represent anyforeign signal that can affect communications between communicationdevices (e.g., a UE served by a particular base station).

As shown in FIG. 4, the signal reception path of the base station 16,which was described as receiving interference from the mobile unit 12 inconjunction with FIG. 1, includes an antenna 50 that provides signals toan amplifier 52. The output of the amplifier 52 is coupled to a diplexer54 that splits the signal from the amplifier 52 into a number ofdifferent paths, one of which may be coupled to an adaptive front end 56and another of which may be coupled to a receiver A 58. The output ofthe adaptive front end 56 is coupled to a receiver B 59, which may, forexample, be embodied in a CDMA receiver or any other suitable receiverB. Although only one signal path is shown in FIG. 4, it will be readilyunderstood to those having ordinary skill in the art that such a signalpath is merely exemplary and that, in reality, a base station mayinclude two or more such signal paths that may be used to process mainand diversity signals received by the base station 16.

It will be readily understood that the illustrations of FIG. 4 can alsobe used to describe the components and functions of other forms ofcommunication devices such as a small cell base station, a microcellbase station, a picocell base station, a femto cell, a WiFi router oraccess point, a cellular phone, a smartphone, a laptop computer, atablet, or other forms of wireless communication devices suitable forapplying the principles of the subject disclosure. Accordingly, suchcommunication devices can include variants of the components shown inFIG. 4 and perform the functions that will be described below. Forillustration purposes only, the descriptions below will address the basestation 16 with an understanding that these embodiments are exemplaryand non-limiting to the subject disclosure.

Referring back to FIG. 4, the outputs of the receiver A 58 and thereceiver B 59 can be coupled to other systems within the base station16. Such systems may perform voice and/or data processing, callprocessing or any other desired function. Additionally, the adaptivefront end module 56 may also be communicatively coupled, via theInternet, telephone lines, cellular network, or any other suitablecommunication systems, to a reporting and control facility that isremote from the base station 16. In some networks, the reporting andcontrol facility may be integrated with the switching station 18. Thereceiver A 58 may be communicatively coupled to the switching station 18and may respond to commands that the switching station 18 issues.

Each of the components 50-60 of the base station 16 shown in FIG. 4,except for the adaptive front end module 56, may be found in aconventional cellular base station 16, the details of which are wellknown to those having ordinary skill in the art. It will also beappreciated by those having ordinary skill in the art that FIG. 4 doesnot disclose every system or subsystem of the base station 16 and,rather, focuses on the relevant systems and subsystems to the subjectdisclosure. In particular, it will be readily appreciated that, whilenot shown in FIG. 4, the base station 16 can include a transmissionsystem or other subsystems. It is further appreciated that the adaptivefront end module 56 can be an integral subsystem of a cellular basestation 16, or can be a modular subsystem that can be physically placedin different locations of a receiver chain of the base station 16, suchas at or near the antenna 50, at or near the amplifier 52, or at or nearthe receiver B 59.

During operation of the base station 16, the antenna 50 receives CDMAcarrier signals that are broadcast from the mobile unit 13A, 13B, 13Cand 13D and couples such signals to the amplifier 52, which amplifiesthe received signals and couples the amplified signals to the diplexer54. The diplexer 54 splits the amplified signal from the amplifier 52and essentially places copies of the amplified signal on each of itsoutput lines. The adaptive front end module 56 receives the signal fromthe diplexer 54 and, if necessary, filters the CDMA carrier signal toremove any undesired interference and couples the filtered CDMA carriersignal to the receiver B 59.

As noted previously, FIG. 2 illustrates an ideal frequency spectrum 40of a CDMA carrier signal that may be received at the antenna 50,amplified and split by the amplifier 52 and the diplexer 54 and coupledto the adaptive front end module 56. If the CDMA carrier signal receivedat the antenna 50 has a frequency spectrum 40 as shown in FIG. 2 withoutany interference, the adaptive front end will not filter the CDMAcarrier signal and will simply couple the signal directly through theadaptive front end module 56 to the receiver B 59.

However, as noted previously, it is possible that the CDMA carriersignal transmitted by the mobile units 13A-13D and received by theantenna 50 has a frequency spectrum as shown in FIG. 3 which contains amulti-carrier CDMA signal 42 that includes not only the four CDMAcarriers 42A, 42B, 42C and 42D from the mobile units 13A, 13B, 13C and13D having unequal CDMA carrier strengths, but also includes interferer46, as shown in FIG. 3, which in this illustration is caused by mobileunit 12. If a multi-carrier CDMA signal having a multi-carrier CDMAsignal 42 including interferer 46 is received by the antenna 50 andamplified, split and presented to the adaptive front end module 56, itwill filter the multi-carrier CDMA signal 42 to produce a filteredfrequency spectrum 43 as shown in FIG. 5.

The filtered multi-carrier CDMA signal 43 has the interferer 46 removed,as shown by the notch 46A. The filtered multi-carrier CDMA signal 43 isthen coupled from the adaptive front end module 56 to the receiver B 59,so that the filtered multi-carrier CDMA signal 43 may be demodulated.Although some of the multi-carrier CDMA signal 42 was removed duringfiltering by the adaptive front end module 56, sufficient multi-carrierCDMA signal 43 remains to enable the receiver B 59 to recover theinformation that was broadcast by mobile unit(s). Accordingly, ingeneral terms, the adaptive front end module 56 selectively filtersmulti-carrier CDMA signals to remove interference therefrom. Furtherdetail regarding the adaptive front end module 56 and its operation isprovided below in conjunction with FIGS. 6-21.

FIG. 6 depicts another example embodiment of the adaptive front endmodule 56. As noted earlier, the adaptive front end module 56 can beutilized by any communication device including cellular phones,smartphones, tablets, small base stations, femto cells, WiFi accesspoints, and so on. In the illustration of FIG. 3, the adaptive front endmodule 56 can include a radio 60 comprising two stages, a receiver stage62 and a transmitter stage 64, each coupled to an antenna assembly 66,66′, which may comprise one of more antennas for the radio 60. The radio60 has a first receiver stage coupled to the antenna assembly 66 andincludes an adaptive front-end controller 68 that receives the input RFsignal from the antenna and performs adaptive signal processing on thatRF signal before providing the modified RF signal to ananalog-to-digital converter 70, which then passes the adapted RF signalto a digital RF tuner 72.

As shown in FIG. 6, the adaptive front end controller 68 of the receiverstage 62 includes two RF signal samplers 74, 76 connected between an RFadaptive filter stage 78 that is controlled by controller 80. Theadaptive filter stage 78 may have a plurality of tunable digital filtersthat can sample an incoming signal and selectively provide band pass orband stop signal shaping of an incoming RF signal, whether it is anentire communication signal or a sub-band signal or various combinationsof both. A controller 80 is coupled to the samplers 74, 76 and filterstage 78 and serves as an RF link adapter that along with the sampler 74monitors the input RF signal from the antenna 66 and determines variousRF signal characteristics such as the interferences and noise within theRF signal. The controller 80 is configured to execute any number of avariety of signal processing algorithms to analyze the received RFsignal, and determine a filter state for the filter stage 78.

By providing tuning coefficient data to the filter stage 78, theadaptive front end controller 68 acts to pre-filter the received RFsignal before the signal is sent to the RF tuner 72, which analyzes thefiltered RF signal for integrity and/or for other applications such ascognitive radio applications. After filtering, the radio tuner 72 maythen perform channel demodulation, data analysis, and local broadcastingfunctions. The RF tuner 72 may be considered the receiver side of anoverall radio tuner, while RF tuner 72′ may be considered thetransmitter side of the same radio tuner. Prior to sending the filteredRF signal, the sampler 76 may provide an indication of the filtered RFsignal to the controller 80 in a feedback manner for further adjustingof the adaptive filter stage 78.

In some examples, the adaptive front-end controller 68 is synchronizedwith the RF tuner 72 by sharing a master clock signal communicatedbetween the two. For example, cognitive radios operating on a 100 μsresponse time can be synchronized such that for every clock cycle theadaptive front end analyzes the input RF signal, determines an optimalconfiguration for the adaptive filter stage 78, filters that RF signalinto the filtered RF signal and communicates the same to the radio tuner72 for cognitive analysis at the radio. By way of example, cellularphones may be implemented with a 200 μs response time on filtering. Byimplementing the adaptive front end controller 68 using a fieldprogrammable gate array configuration for the filter stage, wirelessdevices may identify not only stationary interference, but alsonon-stationary interference, of arbitrary bandwidths on that movinginterferer.

In some implementations, the adaptive front-end controller 68 may filterinterference or noise from the received incoming RF signal and pass thatfiltered RF signal to the tuner 72. In other examples, such as cascadedconfigurations in which there are multiple adaptive filter stages, theadaptive front-end controller 68 may be configured to apply the filteredsignal to an adaptive band pass filter stage to create a passbandportion of the filtered RF signal. For example, the radio tuner 72 maycommunicate information to the controller 80 to instruct the controllerthat the radio is only looking at a portion of an overall RF spectrumand thus cause the adaptive front-end controller 68 not to filtercertain portions of the RF spectrum and thereby band pass only thoseportions. The integration between the radio tuner 72 and the adaptivefront-end controller 68 may be particularly useful in dual-band andtri-band applications in which the radio tuner 72 is able to communicateover different wireless standards, such as GSM, UMTS, or LTE standards.

The algorithms that may be executed by the controller 80 are not limitedto interference detection and filtering of interference signals. In someconfigurations the controller 80 may execute a spectral blind sourceseparation algorithm that looks to isolate two sources from theirconvolved mixtures. The controller 80 may execute a signal tointerference noise ratio (SINR) output estimator for all or portions ofthe RF signal. The controller 80 may perform bidirectional transceiverdata link operations for collaborative retuning of the adaptive filterstage 78 in response to instructions from the radio tuner 72 or fromdata the transmitter stage 64. The controller 80 can determine filtertuning coefficient data for configuring the various adaptive filters ofstage 78 to properly filter the RF signal. The controller 80 may alsoinclude a data interface communicating the tuning coefficient data tothe radio tuner 72 to enable the radio tuner 72 to determine filteringcharacteristics of the adaptive filter 78.

In one embodiment the filtered RF signal may be converted from a digitalsignal to an analog signal within the adaptive front-end controller 68.This allows the controller 80 to integrate in a similar manner toconventional RF filters. In other examples, a digital interface may beused to connect the adaptive front-end controller 68 with the radiotuner 72, in which case the ADC 70 would not be necessary.

The above discussion is in the context of the receiver stage 62. Similarelements are shown in the transmitter stage 64, but bearing a prime. Theelements in the transmitter stage 64 may be similar to those of thereceiver 62, with the exception of the digital to analog converter (DAC)70′ and other adaptations to the other components shown with a prime inthe reference numbers. Furthermore, some or all of these components mayin fact be executed by the same corresponding structure in the receiverstage 62. For example, the RF receiver tuner 72 and the transmittertuner 72′ may be performed by a single tuner device. The same may betrue for the other elements, such as the adaptive filter stages 78 and78′, which may both be implemented in a single FPGA, with differentfilter elements in parallel for full duplex (simultaneous) receive andtransmit operation.

FIG. 7 illustrates another example implementation of an adaptivefront-end controller 100. Input RF signals are received at an antenna(not shown) and coupled to an initial analog filter 104, such as lownoise amplifier (LNA) block, then digitally converted via an analog todigital converter (ADC) 106, prior to the digitized input RF signalbeing coupled to a field programmable gate array (FPGA) 108. Theadaptive filter stage described above may be implemented within the FPGA108, which has been programmed to contain a plurality of adaptive filterelements tunable to different operating frequencies and frequency bands,and at least some being adaptive from a band pass to a band stopconfiguration or vice versa, as desired. Although an FPGA isillustrated, it will be readily understood that other architectures suchas an application specific integrated circuit (ASIC) or a digital signalprocessor (DSP) may also be used to implement a digital filterarchitecture described in greater detail below.

A DSP 110 is coupled to the FPGA108 and executes signal processingalgorithms that may include a spectral blind source separationalgorithm, a signal to interference noise ratio (SINR) output estimator,bidirectional transceiver data line operation for collaborative retuningof the adaptive filter stage in response to instructions from the tuner,and/or an optimal filter tuning coefficients algorithm.

FPGA 108 is also coupled to a PCI target 112 that interfaces the FPGA108 and a PCI bus 114 for communicating data externally. A system clock118 provides a clock input to the FPGA 108 and DSP 110, therebysynchronizing the components. The system clock 118 may be locally set onthe adaptive front-end controller, while in other examples the systemclaim 118 may reflect an external master clock, such as that of a radiotuner. The FPGA 108, DSP 110, and PCI target 112, designatedcollectively as signal processing module 116, will be described ingreater detail below. In the illustrated example, the adaptive front-endcontroller 100 includes a microcontroller 120 coupled to the PCI bus 114and an operations, alarms and metrics (OA&M) processor 122. Althoughthey are shown and described herein as separate devices that executeseparate software instructions, those having ordinary skill in the artwill readily appreciate that the functionality of the microcontroller120 and the OA&M processor 122 may be merged into a single processingdevice. The microcontroller 120 and the OA&M processor 122 are coupledto external memories 124 and 126, respectively. The microcontroller 120may include the ability to communicate with peripheral devices, and, assuch, the microcontroller 120 may be coupled to a USB port, an Ethernetport, or an RS232 port, among others (though none shown). In operation,the microcontroller 120 may locally store lists of channels havinginterferers or a list of known typically available frequency spectrumbands, as well as various other parameters. Such a list may betransferred to a reporting and control facility or a base station, viathe OA&M processor 122, and may be used for system diagnostic purposes.

The aforementioned diagnostic purposes may include, but are not limitedto, controlling the adaptive front-end controller 100 to obtainparticular information relating to an interferer and re-tasking theinterferer. For example, the reporting and control facility may use theadaptive front-end controller 100 to determine the identity of aninterferer, such as a mobile unit, by intercepting the electronic serialnumber (ESN) of the mobile unit, which is sent when the mobile unittransmits information on the channel. Knowing the identity of theinterferer, the reporting and control facility may contactinfrastructure that is communicating with the mobile unit (e.g., thebase station) and may request the infrastructure to change the transmitfrequency for the mobile unit (i.e., the frequency of the channel onwhich the mobile unit is transmitting) or may request the infrastructureto drop communications with the interfering mobile unit altogether.

Additionally, in a cellular configuration (e.g., a system based on aconfiguration like that of FIG. 1) diagnostic purposes may include usingthe adaptive front-end controller 100 to determine a telephone numberthat the mobile unit is attempting to contact and, optionally handlingthe call. For example, the reporting and control facility may use theadaptive front-end controller 100 to determine that the user of themobile unit was dialing 911, or any other emergency number, and may,therefore, decide that the adaptive front-end controller 100 should beused to handle the emergency call by routing the output of the adaptivefront-end controller 100 to a telephone network.

The FPGA 108 can provide a digital output coupled to a digital to analogconverter (DAC) 128 that converts the digital signal to an analog signalwhich may be provided to a filter 130 to generate a filtered RF outputto be broadcast from the base station or mobile station. The digitaloutput at the FPGA 108, as described, may be one of many possibleoutputs. For example, the FPGA 108 may be configured to output signalsbased on a predefined protocol such as a Gigabit Ethernet output, anopen base station architecture initiative (OBSAI) protocol, or a commonpublic radio interface (CPRI) protocol, among others.

It is further noted that the aforementioned diagnostic purposes may alsoinclude creating a database of known interferers, the time of occurrenceof the interferers, the frequency of occurrence of the interferers,spectral information relating to the interferers, a severity analysis ofthe interferers, and so on. The identity of the interferers may be basedsolely on spectral profiles of each interferer that can be used foridentification purposes. Although the aforementioned illustrationsdescribe a mobile unit 12 as an interferer, other sources ofinterference are possible. Any electronic appliance that generateselectromagnetic waves such as, for example, a computer, a set-top box, achild monitor, a wireless access point (e.g., WiFi, ZigBee, Bluetooth,etc.) can be a source of interference. In one embodiment, a database ofelectronic appliances can be analyzed in a laboratory setting or othersuitable testing environment to determine an interference profile foreach appliance. The interference profiles can be stored in a databaseaccording to an appliance type, manufacturer, model number, and otherparameters that may be useful in identifying an interferer. Spectralprofiles provided by, for example, the OA&M processor 108 to adiagnostic system can be compared to a database of previouslycharacterized interferers to determine the identity of the interferencewhen a match is detected.

A diagnostic system, whether operating locally at the adaptive front endcontroller, or remotely at a base station, switching station, or serversystem, can determine the location of the interferer near the basestation (or mobile unit) making the detection, or if a more preciselocation is required, the diagnostic system can instruct several basestations (or mobile units) to perform triangulation analysis to moreprecisely locate the source of the interference if the interference isfrequent and measureable from several vantage points. With locationdata, interference identity, timing and frequency of occurrence, thediagnostic system can generate temporal and geographic reports showinginterferers providing field personnel a means to assess the volume ofinterference, its impact on network performance, and it may providesufficient information to mitigate interference by means other thanfiltering, such as, for example, interference avoidance by way ofantenna steering at the base station, beam steering, re-tasking aninterferer when possible, and so on.

FIG. 8 illustrates further details of an example implementation of asignal processing module 116 that may serve as another embodiment of anadaptive front end controller, it being understood that otherarchitectures may be used to implement a signal detection algorithm. Adecoder 150 receives an input from the ADC 106 and decodes the incomingdata into a format suitable to be processed by the signal processingmodule 116. A digital down converter 152, such as a polyphase decimator,down converts the decoded signal from the decoder 150. The decodedsignal is separated during the digital down conversion stage into acomplex representation of the input signal, that is, into In-Phase (I)and Quadrature-Phase (Q) components which are then fed into a tunableinfinite impulse response (IIR)/finite impulse response (FIR) filter154. The IIR/FIR filter 154 may be implemented as multiple cascaded orparallel IIR and FIR filters. For example, the IIR/FIR filter 154 may beused with multiple filters in series, such as initial adaptive band passfilter followed by adaptive band stop filter. For example, the band passfilters may be implemented as FIR filters, while the band stop filtersmay be implemented as IIR filters. In an embodiment, fifteen cascadedtunable IIR/FIR filters are used to optimize the bit width of eachfilter. Of course other digital down converters and filters such ascascaded integrator-comb (CIC) filters may be used, to name a few. Byusing complex filtering techniques, such as the technique describedherein, the sampling rate is lowered thereby increasing (e.g., doubling)the bandwidth that the filter 154 can handle. In addition, using complexarithmetic also provides the signal processing module 116 the ability toperform higher orders of filtering with greater accuracy.

The I and Q components from the digital down converter 152 are providedto the DSP 110 which implements a detection algorithm and in responseprovides the tunable IIR/FIR filter 154 with tuning coefficient datathat tunes the IIR and/or FIR filters 154 to specific notch (or bandstop) and/or band pass frequencies, respectively, and specificbandwidths. The tuning coefficient data, for example, may include afrequency and a bandwidth coefficient pair for each of the adaptivefilters, which enables the filter to tune to a frequency for band passor band stop operation and the bandwidth to be applied for thatoperation. The tuning coefficient data corresponding to a band passcenter frequency and bandwidth may be generated by the detectionalgorithm and passed to a tunable FIR filter within the IIR/FIR filter154. The filter 154 may then pass all signals located within a passbandof the given transmission frequency. Tuning coefficient datacorresponding to a notch (or band stop) filter may be generated by thedetection algorithm and then applied to an IIR filter within the IIR/FIRfilter 154 to remove any interference located within the passband of theband pass filter. The tuning coefficient data generated by the detectionalgorithm are implemented by the tunable IIR/FIR filters 154 usingmathematical techniques known in the art. In the case of a cognitiveradio, upon implementation of the detection algorithm, the DSP 110 maydetermine and return coefficients corresponding to a specific frequencyand bandwidth to be implemented by the tunable IIR/FIR filter 154through a DSP/PCI interface 158. Similarly, the transfer function of anotch (or band stop) filter may also be implemented by the tunableIIR/FIR filter 154. Of course other mathematical equations may be usedto tune the IIR/FIR filters 154 to specific notch, band stop, or bandpass frequencies and to a specific bandwidth.

After the I and Q components are filtered to the appropriate notch (orband stop) or band pass frequency at a given bandwidth, a digital upconverter 156, such as a polyphase interpolator, converts the signalback to the original data rate, and the output of the digital upconverter is provided to the DAC 128.

A wireless communication device capable to be operated as a dual- ortri-band device communicating over multiple standards, such as over UMTSand LTE may use the adaptive digital filter architecture embodiments asdescribed above. For example, a dual-band device (using both LTE andUMTS) may be preprogrammed within the DSP 110 to transmit first on LTE,if available, and on UMTS only when outside of a LTE network. In such acase, the IIR/FIR filter 154 may receive tuning coefficient data fromthe DSP 110 to pass all signals within a LTE range. That is, the tuningcoefficient data may correspond to a band pass center frequency andbandwidth adapted to pass only signals within the LTE range. The signalscorresponding to a UMTS signal may be filtered, and any interferencecaused by the UMTS signal may be filtered using tuning coefficients,received from the DSP 110, corresponding to a notch (or band stop)frequency and bandwidth associated with the UMTS interference signal.

Alternatively, in some cases it may be desirable to keep the UMTS signalin case the LTE signal fades quickly and the wireless communicationdevice may need to switch communication standards rapidly. In such acase, the UMTS signal may be separated from the LTE signal, and bothpassed by the adaptive front-end controller. Using the adaptive digitalfilter, two outputs may be realized, one output corresponding to the LTEsignal and one output corresponding to a UMTS signal. The DSP 110 may beprogrammed to again recognize the multiple standard service and maygenerate tuning coefficients corresponding to realize a filter, such asa notch (or band stop) filter, to separate the LTE signal from the UMTSsignal. In such examples, an FPGA may be programmed to have paralleladaptive filter stages, one for each communication band.

To implement the adaptive filter stages, in some examples, the signalprocessing module 116 is pre-programmed with general filter architecturecode at the time of production, for example, with parameters definingvarious filter types and operation. The adaptive filter stages may thenbe programmed, through a user interface or other means, by the serviceproviders, device manufactures, etc., to form the actual filterarchitecture (parallel filter stages, cascaded filter stages, etc.) forthe particular device and for the particular network(s) under which thedevice is to be used. Dynamic flexibility can be achieved duringruntime, where the filters may be programmed to different frequenciesand bandwidths, each cycle, as discussed herein.

One method of detecting a signal having interference is by exploitingthe noise like characteristics of a signal. Due to such noise likecharacteristics of the signal, a particular measurement of a channelpower gives no predictive power as to what the next measurement of thesame measurement channel may be. In other words, consecutiveobservations of power in a given channel are un-correlated. As a result,if a given measurement of power in a channel provides predictive powerover subsequent measurements of power in that particular channel, thusindicating a departure from statistics expected of a channel withoutinterference, such a channel may be determined to contain interference.

FIG. 9 illustrates an IS-95 CDMA signal 202, which is a generic DirectSequence Spread Spectrum (DSSS) signal. The CDMA signal 202 may have abandwidth of 1.2288 MHz and it may be used to carry up to 41 channels,each of which has a bandwidth of 30 kHz. One way to identifyinterference affecting the CDMA signal 202 may be to identify any ofsuch 41 channels having excess power above an expected power of the CDMAsignal 202. FIG. 9 also illustrates the probability distributionfunctions (PDFs) 204 of a typical DSSS signal and a complementarycumulative distribution functions (CCDFs) 206 of a typical DSSS signal,which may be used to establish a criteria used to determine channelsdisposed within a signal and having excess power.

Specifically, the PDFs 204 include probability distribution of power ina given channel, which is the likelihood p(x) of measuring a power x ina given channel, for a DSSS signal carrying one mobile unit (212), for aDSSS signal carrying ten mobile units (214), and for a DSSS signalcarrying twenty mobile units (210). For example, for the PDF 212,representing a DSSS signal carrying one mobile unit, the distributionp(x) is observed to be asymmetric, with an abbreviated high power tail.In this case, any channel having power higher than the high power tailof the PDF 212 may be considered to have an interference signal.

The CCDFs 206 denote the likelihood that a power measurement in achannel will exceed a given mean power a, by some value α/σ, wherein σis standard deviation of the power distribution. Specifically, the CCDFs206 include an instance of CCDF for a DSSS signal carrying one mobileunit (220), an instance of CCDF for a DSSS signal carrying ten mobileunits (222), and an instance of CCDF for a DSSS signal carrying twentymobile units (224). Thus, for example, for a DSSS signal carrying onemobile unit, the likelihood of any channel having the ratio α/σ of 10 dBor more is 0.01%. Therefore, an optimal filter can be tuned to such achannel having excess power.

One method of detecting such a channel having interference is byexploiting the noise like characteristic of a DSSS signal. Due to suchnoise like characteristic of DSSS signal, a particular measurement of achannel power gives no predictive power as to what the next measurementof the same measurement channel may be. In other words, consecutiveobservations of power in a given channels are un-correlated. As aresult, if a given measurement of power in a channel provides predictivepower over subsequent measurements of power in that particular channel,thus indicating a departure from statistics expected of a channelwithout interference, such a channel may be determined to containinterference.

FIG. 10 illustrates a flowchart of an interference detection program 300that may be used to determine location of interference in a DSSS signal.At block 302 a series of DSSS signals can be scanned by the adaptivefront end controller described above and the observed values of thesignal strengths can be stored for each of various channels located inthe DSSS signal. For example, at block 302 the adaptive front endcontroller may continuously scan the 1.2288 MHz DSSS signal 60 for eachof the 41 channels dispersed within it. The adaptive front endcontroller may be implemented by any well-known analog scanner ordigital signal processor (DSP) used to scan and store signal strengthsin a DSSS signal. The scanned values of signal strengths may be storedin a memory of such DSP or in any other computer readable memory. Theadaptive front end controller may store the signal strength of aparticular channel along with any information, such as a numericidentifier, identifying the location of that particular channel withinthe DSSS signal.

At block 304 the adaptive front end controller can determine the numberof sequences m of a DSSS signal that may be required to be analyzed todetermine channels having interference. A user may provide such a numberm based on any predetermined criteria. For example, a user may provide mto be equal to four, meaning that four consecutive DSSS signals need tobe analyzed to determine if any of the channels within that DSSS signalspectrum includes an interference signal. As one of ordinary skill inthe art would appreciate, the higher is the selected value of m, themore accurate will be the interference detection. However, the higherthe number m is, the higher is the delay in determining whether aparticular DSSS signal had an interference present in it, subsequently,resulting in a longer delay before a filter is applied to the DSSSsignal to remove the interference signal.

Generally, detection of an interference signal may be performed on arolling basis. That is, at any point in time, m previous DSSS signalsmay be used to analyze presence of an interference signal. The earliestof such m interference signals may be removed from the set of DSSSsignals used to determine the presence of an interference signal on afirst-in-first-out basis. However, in an alternate embodiment, analternate sampling method for the set of DSSS signals may also be used.

At block 306 the adaptive front end controller can select x channelshaving the highest signal strength from each of the m most recent DSSSsignals scanned at the block 302. The number x may be determined by auser. For example, if x is selected to be equal to three, the block 306may select three highest channels from each of the m most recent DSSSsignals. The methodology for selecting x channels having highest signalstrength from a DSSS signal is described in further detail in FIG. 11below. For example, the adaptive front end controller at block 306 maydetermine that the first of the m DSSS signals has channels 10, 15 and27 having the highest signal strengths, the second of the m DSSSchannels has channels 15 and 27 and 35 having the highest signalstrengths, and the third of the m DSSS channels has the channels 15, 27and 35 having the highest signal strength.

After having determined the x channels having the highest signalstrengths in each of the m DSSS signals, at block 308 the adaptive frontend controller can compare these x channels to determine if any of thesehighest strength channels appear more than once in the m DSSS signals.In case of the example above, the adaptive front end controller at block308 may determine that the channels 15 and 27 are present among thehighest strength channels for each of the last three DSSS signals, whilechannel 35 is present among the highest strength channels for at leasttwo of the last three DSSS signals.

Such consistent appearance of channels having highest signal strengthover subsequent DSSS signals indicate that channels 15 and 27, andprobably the channel 35, may have an interference signal super-imposedon them. At block 310 the adaptive front end controller may use suchinformation to determine which channels may have interference. Forexample, based on the number of times a given channel appears in theselected highest signal strength channels, the adaptive front endcontroller at block 310 may determine the confidence level that may beassigned to a conclusion that a given channel contains an interferencesignal.

Alternatively, at block 310 the adaptive front end controller maydetermine a correlation factor for each of the various channelsappearing in the x selected highest signal strength channels and comparethe calculated correlation factors with a threshold correlation factorto determine whether any of the x selected channels has correlatedsignal strengths. Calculating a correlation factor based on a series ofobservations is well known to those of ordinary skill in the art andtherefore is not illustrated in further detail herein. The thresholdcorrelation factor may be given by the user of the interferencedetection program 300.

Note that while in the above illustrated embodiment, the correlationfactors of only the selected highest signal strength channels arecalculated, in an alternate embodiment, correlation factors of all thechannels within the DSSS signals may be calculated and compared to thethreshold correlation factor.

Empirically, it may be shown that when m is selected to be equal tothree, for a clean DSSS signal, the likelihood of having at least onematch among the higher signal strength channels is 0.198, the likelihoodof having at least two matches among the higher signal strength channelsis 0.0106, and the likelihood of having at least three matches among thehigher signal strength channels is 9.38×10⁻⁵. Thus, the higher thenumber of matches, the lesser is the likelihood of having adetermination that one of the x channels contains an interference signal(i.e., a false positive interference detection). It may be shown that ifthe number of scans m is increased to, say four DSSS scans, thelikelihood of having such matches in m consecutive scans is evensmaller, thus providing higher confidence that if such matches are foundto be present, they indicate presence of interference signal in thosechannels.

To identify the presence of interference signals with even higher levelof confidence, at block 312 the adaptive front end controller may decidewhether to compare the signal strengths of the channels determined tohave an interference signal with a threshold. If at block 312 theadaptive front end controller decides to perform such a comparison, atblock 314 the adaptive front end controller may compare the signalstrength of each of the channels determined to have an interference witha threshold level. Such comparing of the channel signal strengths with athreshold may provide added confidence regarding the channel having aninterference signal so that when a filter is configured according to thechannel, the probability of removing a non-interfering signal isreduced. However, a user may determine that such added confidence levelis not necessary and thus no such comparison to a threshold needs to beperformed. In which case, at block 316 the adaptive front end controllerstores the interference signals in a memory.

After storing the information about the channels having interferencesignals, at block 318 the adaptive front end controller selects the nextDSSS signal from the signals scanned and stored at block 302. At block318 the adaptive front end controller may cause the first of the m DSSSsignals to be dropped and the newly added DSSS signal is added to theset of m DSSS signals that will be used to determine presence of aninterference signal (first-in-first-out). Subsequently, at block 306 theprocess of determining channels having interference signals is repeatedby the adaptive front end controller. Finally, at block 320 the adaptivefront end controller may select and activate one or more filters thatare located in the path of the DSSS signal to filter out any channelidentified as having interference in it.

Now referring to FIG. 11, a flowchart illustrates a high strengthchannels detection program 350 that may be used to identify variouschannels within a given scan of the DSSS signal that may contain aninterference signal. The high strength channels detection program 350may be used to implement the functions performed at block 306 of theinterference detection program 300. In a manner similar to theinterference detection program 300, the high strength channels detectionprogram 350 may also be implemented using software, hardware, firmwareor any combination thereof.

At block 352 the adaptive front end controller may sort signal strengthsof each of the n channels within a given DSSS signal. For example, if aDSSS signal has 41 channels, at block 352 the adaptive front endcontroller may sort each of the 41 channels according to its signalstrengths. Subsequently, at block 354 the adaptive front end controllermay select the x highest strength channels from the sorted channels andstore information identifying the selected x highest strength channelsfor further processing. An embodiment of the high strength channelsdetection program 350 may simply use the selected x highest strengthchannels from each scan of the DSSS signals to determine any presence ofinterference in the DSSS signals. However, in an alternate embodiment,additional selected criteria may be used.

Subsequently, at block 356 the adaptive front end controller candetermine if it is necessary to compare the signal strengths of the xhighest strength channels to any other signal strength value, such as athreshold signal strength, etc., where such a threshold may bedetermined using the average signal strength across the DSSS signal. Forexample, at block 356 the adaptive front end controller may use acriterion such as, for example: “when x is selected to be four, if atleast three out of four of the selected channels have also appeared inprevious DSSS signals, no further comparison in necessary.” Anothercriterion may be, for example: “if any of the selected channels islocated at the fringe of the DSSS signal, the signal strengths of suchchannels should be compared to a threshold signal strength.” Otheralternate criteria may also be provided.

If at block 356 the adaptive front end controller determines that nofurther comparison of the signal strengths of the selected x channels isnecessary, at block 358 the adaptive front end controller storesinformation about the selected x channels in a memory for furtherprocessing. If at block 356 the adaptive front end controller determinesthat it is necessary to apply further selection criteria to the selectedx channels, the adaptive front end controller returns to block 360. Atblock 360 the adaptive front end controller may determine a thresholdvalue against which the signal strengths of each of the x channels arecompared based on a predetermined methodology.

For example, in an embodiment, at block 360 the adaptive front endcontroller may determine the threshold based on the average signalstrength of the DSSS signal. The threshold signal strength may be theaverage signal strength of the DSSS signal or a predetermined value maybe added to such average DSSS signal to derive the threshold signalstrength.

Subsequently, at block 362 the adaptive front end controller may comparethe signal strengths of the selected x channels to the threshold valuedetermined at block 360. Only the channels having signal strengthshigher than the selected threshold are used in determining presence ofinterference in the DSSS signal. Finally, at block 364 the adaptivefront end controller may store information about the selected x channelshaving signal strengths higher than the selected threshold in a memory.As discussed above, the interference detection program 300 may use suchinformation about the selected channels to determine the presence ofinterference signal in the DSSS signal.

The interference detection program 300 and the high strength channeldetection program 350 may be implemented by using software, hardware,firmware or any combination thereof. For example, such programs may bestored on a memory of a computer that is used to control activation anddeactivation of one or more notch filters. Alternatively, such programsmay be implemented using a digital signal processor (DSP) whichdetermines the presence and location of interference channels in adynamic fashion and activates/de-activates one or more filters.

FIG. 12 illustrates a three dimensional graph 370 depicting several DSSSsignals 372-374 over a time period. A first axis of the graph 370illustrates the number of channels of the DSSS signals 372-374, a secondaxis illustrates time over which a number of DSSS signals 372-374 arescanned, and a third axis illustrates the power of each of the channels.The DSSS signals 372-374 are shown to be affected by an interferencesignal 378.

The interference detection program 370 may start scanning various DSSSsignals 372-374 starting from the first DSSS signal 372. As discussedabove at block 304 the adaptive front end controller determines thenumber m of the DSSS signals 372-374 that are to be scanned. Because theinterference signal 378 causes the signal strength of a particularchannel to be consistently higher than the other channels for a numberof consecutive scans of the DSSS signals 372-374 at block 210 theadaptive front end controller identifies a particular channel having aninterference signal present. Subsequently, at block 320 the adaptivefront end controller will select and activate a filter that applies thefilter function as described above, to the channel having interference.

The graph 370 also illustrates the average signal strengths of each ofthe DSSS signals 372-374 by a line 376. As discussed above, at block 362the adaptive front end controller may compare the signal strengths ofeach of the x selected channels from the DSSS signals 372-374 with theaverage signal strength, as denoted by line 376, in that particular DSSSsignal.

Now referring to FIG. 13, a graph 380 illustrates interference detectionsuccess rate of using the interference detection program 370, as afunction of strength of an interference signal affecting a DSSS signal.The x-axis of the graph 380 depicts the strength of interference signalrelative to the strength of the DSSS signal, while the y-axis depictsthe detection success rate in percentages. As illustrated, when aninterference signal has a strength of at least 2 dB higher than thestrength of the DSSS signal, such an interference signal is detectedwith at least ninety five percent success rate.

The foregoing interference detection and mitigation embodiments canfurther be adapted for detecting and mitigating interference inlong-term evolution (LTE) communication systems.

LTE transmission consists of a combination of Resource Blocks (RB's)which have variable characteristics in frequency and time. A single RBcan be assigned to a user equipment, specifically, a 180 KHz continuousspectrum utilized for 0.5-1 msec. An LTE band can be partitioned into anumber of RBs which could be allocated to individual communicationdevices for specified periods of time for LTE transmission.Consequently, an LTE spectrum has an RF environment dynamically variablein frequency utilization over time. FIG. 14 depicts an illustrative LTEtransmission.

LTE utilizes different media access methods for downlink (orthogonalfrequency-division multiple access; generally, referred to as OFDMA) anduplink (single carrier frequency-division multiple access; generally,referred to as SC-1-DMA). For downlink communications, each RB contains12 sub-carriers with 15 KHz spacing. Each sub-carrier can be used totransmit individual bit information according to the OFDMA protocol. Foruplink communications, LTE utilizes a similar RB structure with 12sub-carriers, but in contrast to downlink, uplink data is pre-coded forspreading across 12 sub-carriers and is transmitted concurrently on all12 sub-carriers.

The effect of data spreading across multiple sub-carriers yields atransmission with spectral characteristics similar to a CDMA/UMTSsignal. Hence, similar principles of interference detection can beapplied within an instance of SC-1-DMA transmission from an individualcommunication device—described herein as user equipment (UE). However,since each transmission consists of unknown RB allocations with unknowndurations, such a detection principle can only be applied separately foreach individual RB within a frequency and specific time domain. If aparticular RB is not used for LTE transmission at the time of detection,the RF spectrum will present a thermal noise which adheres to thecharacteristics of a spread spectrum signal, similar to a CDMA/UMTSsignal.

Co-channel, as well as other forms of interference, can causeperformance degradation to SC-FDMA and OFDMA signals when present. FIG.15 depicts an illustration of an LTE transmission affected byinterferers 402, 404, 406 and 408 occurring at different points in time.Since such LTE transmissions do not typically have flat power spectraldensities (see FIG. 14), identification of interference as shown in FIG.15 can be a difficult technical problem. The subject disclosure,presents a method to improve the detection of interference inSC-FDMA/OFDM channels through a time-averaging algorithm that isolatesinterference components in the channel and ignores the underlyingsignal.

Time averaging system (TAS) can be achieved with a boxcar (rolling)average, in which the TAS is obtained as a linear average of a Q ofprevious spectrum samples, with Q being a user-settable parameter. The Qvalue determines the “strength” of the averaging, with higher Q valueresulting in a TAS that is more strongly smoothed in time and lessdependent on short duration transient signals. Due to thefrequency-hopped characteristic of SC-FDMA/OFDMA signals, which arecomposed of short duration transients, the TAS of such signals isapproximately flat. It will be appreciated that TAS can also beaccomplished by other methods such as a forgetting factor filter.

In one embodiment, an adaptive threshold can be determined by a method500 as depicted in FIG. 16. Q defines how many cycles of t_(i) to use(e.g., 100 cycles can be represented by t₁ thru t₁₀₀). The adaptivefront end module 56 of FIG. 6 can be configured to measure power in 30KHz increments starting from a particular RB and over multiple timecycles. For illustration purposes, the adaptive front end module 56 isassumed to measure power across a 5 MHz spectrum. It will be appreciatedthat the adaptive front end module 56 can be configured for otherincrements (e.g., 15 KHz or 60 KHz), and a different RF spectrumbandwidth. With this in mind, the adaptive front end module 56 can beconfigured at frequency increment f1 to measure power at t1, t2, thru tq(q representing the number of time cycles, i.e., Q). At f1+30 kHz, theadaptive front end module 56 measures power at t1, t2, thru tn. Thefrequency increment can be defined by f0+(z−1)*30 KHz=fz, where f0 is astarting frequency, where z=1 . . . x, and z defines increments of 30KHz increment, e.g., f1=f(z=1) first 30 KHz increment, f2=f(z=2) second30 KHz increment, etc.

The adaptive front end module 56 repeats these steps until the spectrumof interest has been fully scanned for Q cycles, thereby producing thefollowing power level sample sets:

S_(f 1(t 1  thru  tq)) : s _(1, t 1, f 1), s_(2, t 2, f 1), …  , s_(q, tq, f 1)S_(f 2(t 1  thru  tq)) : s _(1, t 1, f 2), s_(2, t 2, f 2), …  , s_(q, tq, f 2)…S_(f x(t 1  thru  tq)) : s _(1, t 1, f z), s_(2, t 2, f x), …  , s_(q, tq, f x)

The adaptive front end module 56 in step 504, calculates averages foreach of the power level sample sets as provided below:

a 1(f 1) = (s_(1, t 1, f 1) + s_(2, t 2, f 1), …  , +s_(q, tq, f 1))/qa 2(f 2) = (s_(1, t 1, f 2) + s_(2, t 2, f 2), …  , s_(q, tq, f 2))/q…ax(fx) = (s_(1, t 1, f x), s_(2, t 2, f x), …  , s_(q, tq, f x))/q

In one embodiment, the adaptive front end module 56 can be configured todetermine at step 506 the top “m” averages (e.g., the top 3 averages)and dismiss these averages from the calculations. The variable “m” canbe user-supplied or can be empirically determined from fieldmeasurements collected by one or more base stations utilizing anadaptive front end module 56. This step can be used to avoid skewing abaseline average across all frequency increments from being too high,resulting in a threshold calculation that may be too conservative. Ifstep 506 is invoked, a baseline average can be determined in step 508according to the equation: Baseline Avg=(a1+a2+ . . . +az−averages thathave been dismissed)/(x−m). If step 506 is skipped, the baseline averagecan be determined from the equation: Baseline Avg=(a1+a2+ . . . +az)/x.Once the baseline average is determined in step 508, the adaptive frontend module 56 can proceed to step 510 where it calculates a thresholdaccording to the equation: Threshold=ydB offset+Baseline Avg. The ydBoffset can be user defined or empirically determined from fieldmeasurements collected by one or more base stations utilizing anadaptive front end module 56.

Once a cycle of steps 502 through 510 have been completed, the adaptivefront end module 56 can monitor at step 512 interference per frequencyincrement of the spectrum being scanned based on any power levelsmeasured above the threshold 602 calculated in step 510 as shown in FIG.17. Not all interferers illustrated in FIG. 17 exceed the threshold,such as the interferer with reference 610. Although this interferer hasa high power signature, it was not detected because it occurred during aresource block (R4) that was not in use. As such, the interferer 510fell below the threshold 602. In another illustration, interferer s 612also fell below the threshold 602. This interferer was missed because ofits low power signature even though the RB from which it occurred (R3)was active.

Method 500 can utilize any of the embodiments in the illustratedflowcharts described above to further enhance the interferencedetermination process. For example, method 500 of FIG. 16 can be adaptedto apply weights to the power levels, and/or perform correlationanalysis to achieve a desired confidence level that the properinterferers are addressed. For example, with correlation analysis, theadaptive front end module 56 can be configured to ignore interferers 614and 616 of FIG. 17 because their frequency of occurrence is low. Method500 can also be adapted to prioritize interference mitigation.Prioritization can be based on frequency of occurrence of theinterferers, time of day of the interference, the affect theinterference has on network traffic, and/or other suitable factors forprioritizing interference to reduce its impact on the network.Prioritization schemes can be especially useful when the filteringresources of the adaptive front end module 56 can only support a limitednumber of filtering events.

When one or more interferers are detected in step 512, the adaptivefront end module 56 can mitigate the interference at step 514 byconfiguring one or more filters to suppress the one or more interferersas described above. When there are limited resources to suppress allinterferers, the adaptive front end module 56 can use a prioritizationscheme to address the most harmful interference as discussed above. FIG.18 provides an illustration of how the adaptive front end module 56 canbe suppress interferers based on the aforementioned algorithms of thesubject disclosure. For example, interferers 612, 614 and 616 can beignored by the adaptive front end module 56 because their correlationmay be low, while interference suppression is applied for all otherinterferers as shown by reference 650.

In one embodiment, the adaptive front end module 56 can submit a reportto a diagnostic system that includes information relating to theinterferers detected. The report can including among other things, afrequency of occurrence of the interferer, spectral data relating to theinterferer, an identification of the base station from which theinterferer was detected, a severity analysis of the interferer (e.g.,bit error rate, packet loss rate, or other traffic information detectedduring the interferer), and so on. The diagnostic system can communicatewith other base stations with other operable adaptive front end module56 to perform macro analysis of interferers such as triangulation tolocate interferers, identity analysis of interferers based on acomparison of spectral data and spectral profiles of known interferers,and so on.

In one embodiment, the reports provided by the adaptive front end module56 can be used by the diagnostic system to in some instance performavoidance mitigation. For example, if the interferer is known to be acommunication device in the network, the diagnostic system can direct abase station in communication with the communication device to directthe communication device to another channel so as to remove theinterference experienced by a neighboring base station. Alternatively,the diagnostic system can direct an affected base station to utilizebeam steering and or mechanical steering of antennas to avoid aninterferer. When avoidance is performed, the mitigation step 514 can beskipped or may be invoked less as a result of the avoidance steps takenby the diagnostic system.

Once mitigation and/or an interference report has been processed insteps 514 and 516, respectively, the adaptive front end module 56 canproceed to step 518. In this step, the adaptive front end module 56 canrepeat steps 502 thru 510 to calculate a new baseline average andcorresponding threshold based on Q cycles of the resource blocks. Eachcycle creates a new adaptive threshold that is used for interferencedetection. It should be noted that when Q is high, changes to thebaseline average are smaller, and consequently the adaptive thresholdvaries less over Q cycles. In contrast, when Q is low, changes to thebaseline average are higher, which results in a more rapidly changingadaptive threshold.

Generally speaking, one can expect that there will be more noise-freeresource blocks than resource blocks with substantive noise.Accordingly, if an interferer is present (constant or ad hoc), one canexpect the aforementioned algorithm described by method 500 will producean adaptive threshold (i.e., baseline average+offset) that will be lowerthan interferer's power level due to mostly noise-free resource blocksdriving down baseline average. Although certain communication deviceswill have a high initial power level when initiating communications witha base station, it can be further assumed that over time the powerlevels will be lowered to a nominal operating condition. A reasonablyhigh Q would likely also dampen disparities between RB's based on theabove described embodiments.

It is further noted that the aforementioned algorithms can be modifiedwhile maintaining an objective of mitigating detected interference. Forinstance, instead of calculating a baseline average from a combinationof averages a1(f1) through ax(fx) or subsets thereof, the adaptive frontend controller 56 can be configured to calculate a base line average foreach resource block according to a known average of adjacent resourceblocks, an average calculated for the resource block itself, or otherinformation that may be provided by, for example, a resource blockscheduler that may be helpful in calculating a desired baseline averagefor each resource block or groups of resource blocks. For instance, theresource block schedule can inform the adaptive front end module 56 asto which resource blocks are active and at what time periods. Thisinformation can be used by the adaptive front end module 56 determineindividualized baseline averages for each of the resource blocks orgroups thereof. Since baseline averages can be individualized, eachresource block can also have its own threshold applied to the baselineaverage of the resource block. Accordingly, thresholds can vary betweenresource blocks for detecting interferers.

It is further noted that the aforementioned mitigation and detectionalgorithms can be implemented by any communication device includingcellular phones, smartphones, tablets, small base stations, macro basestations, femto cells, WiFi access points, and so on. Small basestations (commonly referred to as small cells) can represent low-poweredradio access nodes that can operate in licensed and/or unlicensedspectrum that have a range of 10 meters to 1 or 2 kilometers, comparedto a macrocell (or macro base station) which might have a range of a fewtens of kilometers. Small base stations can be used for mobile dataoffloading as a more efficient use of radio spectrum.

FIG. 19 depicts an illustrative embodiment of a method 700 formitigating interference such as shown in FIG. 15. Method 700 can beperformed singly or in combination by a mobile communication device, astationary communication device, base stations, and/or a system orsystems in communication with the base stations and/or mobilecommunication devices. Method 700 can begin with step 702, whereinterference is detected in one or more segments of a firstcommunication system. A communication system in the present context canrepresent a base station, such as a cellular base station, a small cell(which can represent a femto cell, or a smaller more portable version ofa cellular base station), a WiFi router, a cordless phone base station,or any other form of a communication system that can providecommunication services (voice, data or both) to fixed or mobilecommunication devices. The terms communication system and base stationmay be used interchangeably below. In either instance, such terms are tobe given a broad interpretation such as described above. A segment canrepresent a resource block or other subsets of communication spectrum ofany suitable bandwidth. For illustration purposes only, segments will bereferred to henceforth as resource blocks. In addition, reference willbe made by a mobile communication device affected by the interference.It is to be understood that method 700 can also be applied to stationarycommunication devices.

Referring back to step 702, the interference occurring in the resourceblock(s) can be detected by a mobile communication device utilizing theadaptive thresholds described in the subject disclosure. The mobilecommunication device can inform the first communication system (hereinreferred to as first base station) that it has detected suchinterference. The interference can also be detected by a base stationthat is in communication with the mobile communication device. The basestation can collect interference information in a database for futurereference. The base station can also transmit the interferenceinformation to a centralized system that monitors interference atmultiple base stations. The interference can be stored and organized ina system-wide database (along with the individual databases of each basestation) according to time stamps when the interference occurred,resource blocks affected by the interference, an identity of the basestation collecting the interference information, an identity of themobile communication device affected by the interference, frequency ofoccurrence of the interference, spectral information descriptive of theinterference, an identity of the interferer if it can be synthesizedfrom the spectral information, and so on.

At step 704, a determination can be made as to the traffic utilizationof resource blocks affected by the interference and other resourceblocks of the first base station that may be unaffected by interferenceor experiencing interference less impactful to communications. In thisstep a determination can be made as to the availability of unusedbandwidth for redirecting data traffic of the mobile communicationdevice affected by the interference to other resource blocks. Datatraffic can represent voice only communications, data onlycommunications, or a combination thereof. If other resource blocks areidentified that can be used to redirect all or a portion of the datatraffic with less interference or no interference at all, then aredirection of at least a portion of the data traffic is possible atstep 706.

At step 708 a further determination can be made whether interferencesuppression by filtering techniques described in the subject disclosurecan be used to avoid redirection and continued use of the resourceblocks currently assigned to the mobile communication device. Quality ofService (QoS), data throughput, and other factors as defined by theservice provider or as defined in a service agreement between asubscriber of the mobile communication device and the service providercan be used to determine whether noise suppression is feasible. If noisesuppression is feasible, then one or more embodiments described in thesubject disclosure can be used in step 710 to improve communications inthe existing resource blocks without redirecting data traffic of themobile communication device.

If, however, noise suppression is not feasible, then the mobilecommunication device can be instructed to redirect at least a portion ofdata traffic to the available resource blocks of the first base stationidentified in step 706. The first base station providing services to themobile communication device can provide these instructions to the mobilecommunication device. However, prior to instructing the mobilecommunication device to redirect traffic, the base station can retrieveinterference information from its database to assess the quality of theavailable resource blocks identified in step 706. If the availableresource blocks have less interference or no interference at all, thenthe base station can proceed to step 712. If, however, there are noavailable resource blocks at step 706, or the available resource blocksare affected by equal or worse noise, then method 700 continues at step714.

In one embodiment, steps 702, 704, 706, 708, 710, and 712 can beperformed by a base station. Other embodiments are contemplated.

In step 714, a second communication system (referred to herein as secondbase station) in a vicinity of the mobile communication device can bedetected. Step 714 can represent the base station that detected theinterference in step 702 informing a central system overlooking aplurality of base stations that filtering or redirection of traffic ofthe affected mobile communication device is not possible. The detectionof the second communication system can be made by the mobilecommunication device, or a determination can be made by the centralsystem monitoring the location of the affected mobile communicationdevice as well as other mobile communication devices according tocoordinate information provided by a GPS receiver of the mobilecommunication devices, and knowledge of a communication range of otherbase stations. At step 716, resource blocks of the second base stationcan be determined to be available for redirecting at least a portion ofthe data traffic of the mobile communication device. At step 718,interference information can be retrieved from a system-wide databasethat stores interference information provided by base stations, or theinterference information can be retrieved from or by the second basestation from its own database. At step 720 a determination can be madefrom the interference information whether the resource blocks of thesecond base station are less affected by interference than theinterference occurring in the resource blocks of the first base station.This step can be performed by a central system that tracks all basestations, or by the affected mobile communication device which canrequest the interference information from the central system, access thesystem-wide database, or access the database of the second base station.

If the interference information indicates the interference in theresource blocks of the second base station tend to be more affected byinterference than the resource blocks of the first base station, thenmethod 700 can proceed to step 714 and repeat the process of searchingfor an alternate base station in a vicinity of the mobile communicationdevice, determining availability of resource blocks for transporting atleast a portion of the data traffic of the mobile communication device,and determining whether noise in these resource blocks is acceptable forredirecting the traffic. It should be noted that the mobilecommunication device can perform noise suppression as described in step710 on the resource blocks of the second base station. Accordingly, instep 720 a determination of whether the interference is acceptable inthe resource blocks of the second base station can include noisesuppression analysis based on the embodiments described in the subjectdisclosure. If an alternate base station is not found, the mobilecommunication device can revert to step 710 and perform noisesuppression on the resource blocks of the first base station to reducepacket losses and/or other adverse effects, and if necessary increaseerror correction bits to further improve communications.

If, on the other hand, at step 722 the interference in the resourceblocks of the second base station is acceptable, then the mobilecommunication device can proceed to step 724 where it initiatescommunication with the second base station and redirects at least aportion (all or some) of the data traffic to the resource blocks of thesecond base station at step 726. In the case of a partial redirection,the mobile communication device may be allocating a portion of the datatraffic to some resource blocks of the first base station and the restto the resource blocks of the second base station. The resource blocksof the first base station may or may not be affected by the interferencedetected in step 702. If the resource blocks of the first base stationbeing used by the mobile communication device are affected by theinterference, such a situation may be acceptable if throughput isnonetheless increased by allocating a portion of the data traffic to theresource blocks of the second base station.

It should be further noted that a determination in step 720 of anacceptable interference level can be the result of no interferenceoccurring in the resource blocks of the second base station, orinterference being present in the resource blocks of the second basestation but having a less detrimental effect than the interferenceexperienced in the resource blocks of the first base station. It shouldbe also noted that the resource blocks of the second base station mayexperience interference that is noticeably periodic and not present inall time slots. Under such circumstances, the periodicity of theinterference may be less harmful than the interference occurring in theresource blocks of the first base station if such interference is morefrequent or constant in time. It is further noted, that a resource blockscheduler of the second base station may assign the resource blocks tothe mobile communication device according to a time slot scheme thatavoids the periodicity of the known interference.

It is contemplated that the steps of method 700 can be rearranged and/orindividually modified without departing from the scope of the claims ofthe subject disclosure. Consequently, the steps of method 700 can beperformed by a mobile communication device, a base station, a centralsystem, or any combination thereof.

Operating a wireless network can require a significant amount of effortto deploy and maintain it successfully. An additional complicationinvolves the addition of new cell sites, sector splits, new frequencybands, technology evolving to new generations, user traffic patternsevolving and growing, and customer expectations for coverage andaccessibility increasing. Such complexities in network design,optimization and adaptation are illustrated by way of example in FIG.20. The underlying physical link that supports such networks isnegatively impacted by changing weather, construction of new buildings,and an increase in operators offering services and devices using thewireless spectrum.

All of these challenges which can impact the operations of a networkcombine to make it harder for users to make calls, transfer data, andenjoy wireless applications. Wireless customers do not necessarilyunderstand the complexity that makes a communication network workproperly. They just expect it to always work. The service provider isleft having to design the best network it can, dealing with all of thecomplexity described above. Tools have been developed to manage in partthis complexity, but the wireless physical link requires specialexpertise. The underlying foundation of the performance of the wirelessnetwork is the physical link, the foundation that services rely upon.Typically, networks are designed to use the OSI seven layer model (shownin FIG. 21), which itself requires a reliable physical layer (referredto herein as the RF link) as a necessary element to achieve a desirableperformance design. Without the RF link network communications would notbe possible.

The RF link is characterized at a cell site deployment stage when cellsites are selected and antenna heights and azimuths are determined.Dimensioning and propagation along with user traffic distribution are astarting point for the RF link. Once a cell site is built andconfigured, further optimization falls into two major categories: RFoptimization/site modifications (e.g., involving adjusting azimuth ortilting antennas, adding low noise amplifiers or LNAs, etc.), andreal-time link adaptation (the way an eNodeB and user equipment (UE) areconstantly informing each other about link conditions and adjustingpower levels, modulation schemes, etc).

The network design along with RF optimization/site modifications areonly altered occasionally and most changes are expensive. Real-time linkadaptation, on the other hand, has low ongoing costs and to the extentpossible can be used to respond in real-time to changes experienced byan RF link (referred to herein as the “link condition”). The aspects ofdesigning, optimizing and running a network are vital and a priority fornetwork operators and wireless network equipment makers. Between networkdesign and real-time adaptation a wide variety of manual and autonomouschanges take place as part of network optimization and self-organizingnetworks.

In addition to the issues described above, there is an unsolved problemimpacting the RF link that is not being addressed well with today'ssolutions, which in turn impacts network performance and the resultingcustomer experience. The subject disclosure addresses this problem bydescribing embodiments for improving the RF physical layer autonomouslywithout relying on traditional cell site modifications. The subjectdisclosure also describes embodiments for monitoring link conditionsmore fully and over greater time windows than is currently performed.Currently, Service Overlay Networks (SON) focus only on downlinkconditioning. The systems and methods of the subject disclosure can beadapted to both uplink and the downlink conditioning. Improvements madeto an uplink by a base station, for example, can be shared with the SONnetwork to perform downlink conditioning and thereby improve downlinkperformance. For example, if the performance of an uplink is improved,the SON can be notified of such improvements and can be provided uplinkperformance data. The SON network can use this information to, forexample, direct the base station to increase coverage by adjusting aphysical position of an antenna (e.g., adjust tilt of the antenna).

Additionally, the systems and methods of the subject disclosure can beadapted to demodulate a transmit link (downlink) to obtain parametricinformation relating to the downlink (e.g., a resource block or RBschedule, gain being used on the downlink, tilt position of the antenna,etc.). In an embodiment, the systems and methods of the subjectdisclosure can be adapted to obtain the downlink parametric informationwithout demodulation (e.g., from a functional module of the basestation). The systems and methods of the subject disclosure can in turnuse the downlink parametric information to improve uplink conditioning.In an embodiment, systems and methods of the subject disclosure can usegain data associated with a downlink, a tilt position or adjustments ofthe downlink antenna, to improve uplink conditioning. In an embodiment,the systems and methods of the subject disclosure can be adapted to usethe RB schedule to determine which RB's are to be observed/measured(e.g., RB's in use by UE's) and which RB's are to be ignored (e.g., RB'snot in use by UE's) when performing uplink conditioning.

Additionally, in a closed-loop system, the embodiments of the subjectdisclosure can be adapted to balance performance between an uplink anddownlink contemporaneously or sequentially. For example, when an antennais physically adjusted (e.g., tilted) the embodiments of the subjectdisclosure can be adapted to determine how such an adjustment affectsthe uplink. If the adjustment is detrimental to the uplink, it can bereversed in whole or in part. If the adjustment has a nominal adverseimpact on the uplink, the adjustment can be preserved or minimallyadjusted. If the adjustment has an adverse impact on the uplink that isnot detrimental but significant, changes to the uplink (e.g., increasinggain, filter scheme on uplink, requesting UEs to change MCS, etc.) canbe identified and initiated to determine if the adjustment to theantenna can be preserved or should be reversed in whole or in part. Inan embodiment, a combination of a partial reversal to the adjustment ofthe antenna and adjustments to the uplink can be initiated to balance aperformance of both the uplink and downlink. Closed-loop concepts suchas these can also be applied to the uplink. In an embodiment, forexample, the downlink can be analyzed in response to changes to theuplink, and adjustments can be performed to the downlink and/or theuplink if the effects are undesirable.

In an embodiment, closed-loop system(s) and method(s) that perform linkconditioning on both the uplink and downlink can be adapted to identifya balanced (“sweet spot”) performance between the uplink and thedownlink such that neither the uplink nor the downlink is at optimal (ormaximum) performance. In an embodiment, a closed-loop system and methodcan be performed by the SON network by receiving conditioninginformation relating to an uplink and/or a downlink from cell sites andby directing a number of such cell sites to perform corrective actionson the uplink, the downlink, or both to balance performancetherebetween. In an embodiment, a closed-loop system and method forbalancing performance between uplinks and downlinks can be performed bycell sites independently, UEs independently, cell sites cooperating withUEs, cell sites cooperating among each other, UEs cooperating among eachother, or combinations thereof with or without assistance of a SONnetwork by analyzing link conditioning performed on the uplinks and/ordownlinks.

In one embodiment, the subject disclosure describes embodiments forimproving network performance by analyzing information collected acrossseveral RF links to holistically improve communications between eNodeBsand UEs. In one embodiment, the subject disclosure describes embodimentsfor obtaining a suite of spectral KPIs (key performance indicators)which better capture the conditions of an RF environment. Such data canbe used in self-optimizing networks to tune the RF link that supportsthe UE/eNodeB relationship. In addition, measurements and adjustmentscan be used to provide self-healing capabilities that enable the RF linkof a UE/eNodeB RF to be adapted in real-time.

In one embodiment, signal to interference plus noise ratio (SINR) is anindicator that can be used to measure a quality of wirelesscommunications between mobile and stationary communication devices suchas base station(s). A base station as described in the subjectdisclosure can represent a communication device that provides wirelesscommunication services to mobile communication devices. A base stationcan include without limitation a macro cellular base station, a smallcell base station, a micro cell base station, a femtocell, a wirelessaccess point (e.g., WiFi, Bluetooth), a Digital Enhanced CordlessTelecommunications (DECT) base station, and other stationary ornon-portable communication services devices. The term “cell site” andbase station may be used interchangeably. A mobile or portablecommunication device can represent any computing device utilizing awireless transceiver for communicating with a base station such as acellular telephone, a tablet, a laptop computer, a desktop computer, andso on.

For illustration purposes only, the embodiments that follow will bedescribed in relation to cellular base stations and mobile cellulartelephones. It is submitted, however, that the embodiments of thesubject disclosure can be adapted for use by communication protocols andcommunication devices that differ from cellular protocols and cellularcommunication devices.

In communication systems such as LTE networks, achieving a target SINRmay enable the coverage area of a cell site to achieve its design goalsand allow the cell site to utilize higher modulation and coding schemes(MCS), which can result in higher spectral density—a desirable goal forLTE networks. Delivering desirable throughput rates in LTE systems canrequire higher SINR than in 3G systems. Performance of LTE systems cansuffer as SINR falls, whether due to lower signal and/or higherinterference and noise. FIG. 22 depicts the impact of SINR on throughputand therefore capacity.

In one embodiment, SINR can be improved by collecting information fromeach cell site (e.g., on a sector and/or resource block basis),compiling an estimated SINR from such information, and adjusting RFparameters of the RF link to improve an overall network performance ofthe cell site. In one embodiment, SINR can be described according to thefollowing equation:

SINR=Interference+Noise/Signal=N+N _(c) +N _(adj) +N _(comp) +N _(out)+ΣI/S  (EQ 1)

where S is the received signal level, N is the thermal noise, and N_(c)is in-band co-channel interference, N_(adj) is the adjacent band noisein guard bands or the operator's other carriers, N_(comp) isinterference in the same overall frequency band from other operators,N_(out) is the out-of-band noise, and ΣI is the summation of theinter-cell interference contributed from surrounding cell sites. Someprior art systems consider in-band co-channel interference N_(c), theadjacent interference noise N_(adj), the competitors' transmissionsN_(comp), and the out of band noise N_(out) to be very small. Thisassumption is generally not accurate, particularly for cell sites whereperformance is a challenge. In practice, I is proportional to thequality and strength of the signal (S) from neighboring sites;particularly, in dense networks or near cell edges, where one site'ssignal is another site's interference.

By describing SINR in its constituent parts as depicted in the aboveequation, specific actions can be taken to improve SINR and consequentlyperformance of one or more RF links, which in turn improves performanceof the network. An RF signal received by a cell site can be improved ina number of ways such as by selective filtering, adding gain oramplification, increasing attenuation, tilting antennas, and adjustingother RF parameters. RE parameters of an RF link can be modified in waysthat improves overall network performance within a specific cell siteand in some cases across multiple inter-related cell sites.

To achieve improvements in one or more cell sites, a matrix of SINRs canbe created that includes an estimate for SINR at a path level for eachnode (cell site) or sector in the network. Optimization scenarios can beachieved by analyzing a network of cell sites collectively using linearprogramming for matrix optimization. By making adjustments to an uplink,one can create a weighted maximization of the SINR matrix with element δadded to each SINR element. Each point in the matrix with index i and jcan consist of SINR_(i,j)+δ_(i,j) for a particular node. In oneembodiment, SINR can be optimized for each cell site, within anacceptable range of SINR_(i,j)±δ_(i,j), where δ_(i,j) is lower than somespecified Δ. The term δ_(i,j) can represent a threshold range ofperformance acceptable to a service provider. A SINR outside of thethreshold range can be identified or flagged as an undesirable SINR. Thethreshold range δ_(i,j) can be the same for all base stations, paths,sectors, or clusters thereof, or can be individualized per base station,path, sector, or clusters thereof. The term Δ can represent a maximumthreshold range which the threshold range δ_(i,j) cannot exceed. Thismaximum threshold range Δ can be applied the same to all base stations,sectors, paths, or clusters thereof. Alternatively, the term Δ candiffer per base station, sector, path, or cluster thereof. In oneembodiment, the objective may not necessarily be to optimize SINR of aparticular cell site. Rather the objective can be to optimize SINR ofmultiple nodes (cell sites and/or sectors) in a network. Below is anequation illustrating a matrix for optimizing SINR of one or more nodes(cell sites).

$\begin{matrix}{\left\lbrack \begin{matrix}{{SINR}_{1,1} \pm \delta_{1,1}} & \ldots & {{SINR}_{1,i} \pm \delta_{1,i}} \\\vdots & \ddots & \vdots \\{{SINR}_{i,1} \pm \delta_{i,1}} & \ldots & {{SINR}_{i,j} \pm \delta_{i,j}}\end{matrix} \right\rbrack  {\quad{{\times \left\lbrack \begin{matrix}{Transformation} \\{matrix}\end{matrix} \right\rbrack} = \left\lbrack \begin{matrix}{Optimized} \\{{SINR} + \delta}\end{matrix} \right\rbrack}}} & \left( {{EQ}\mspace{14mu} 2} \right)\end{matrix}$

In one embodiment, for a particular cell site and sector i,j, SINR canbe estimated on a resource-block level basis as SINR_(i,j,k) (where i,and j are the site and sector indices that refer to the site locationwith respect to the surrounding sites and k is the index that refers toa particular resource block within the LTE system). The overall channelSINR_(i,j), can be calculated by averaging the SINR_(i,j,k) over all theresource blocks, SINR_(i,j)=Σ_(k=1) ^(N) SINR_(i,j,k), where N can be,for example, 50.

Improving SINR of one or more the nodes in a network using the aboveanalysis, can in turn improve the throughput and capacity of thenetwork, thereby enabling higher modulation and coding schemes (MCS) asshown in FIG. 22. The improved link performance of cell site(s) can helpachieve design goals set by service providers for coverage area andcapacity of the cell site(s). Achieving these design goals results inimproved (and at times optimal) throughput and cell coverage as measuredby data rate, accessibility/retainability, and reduction of time UEs arenot on LTE commonly referred to as measure of TNOL (or similarlyincrease time UE's are on LTE).

In one embodiment, a closed loop process can be used for adjusting thecondition of an RF link of a node (or cell site) to improve performanceof one or more other nodes in a network. Such a process is depicted inFIG. 23. This process can be described as follows.

-   -   Measure: collect a set of RF KPIs (Key Performance Indicators)        across multiple categories to more fully reflect the frequently        changing conditions of the underlying RF physical link of one or        more nodes.    -   Analyze: compare current RF link conditions and trends against        network KPIs and the SINR matrix to determine changes that can        be implemented to improve the conditions of the RF physical        link.    -   Do: perform changes to adjust the RF link conditions of one or        more nodes.    -   Check: confirm that the changes that were made have had the        desired effect. To achieve a closed loop process, the results        derived from the “Check” step can be provided to the “Measure”        step in subsequent iterations to drive continuous improvement.

Together the steps of FIG. 23 provide a useful approach for analyzing anRF link and for taking appropriate steps to improve its condition. Thesteps of FIG. 23 are discussed in greater detail below.

Measurement.

Understanding the current conditions of an RF link is an important stepto improving network performance. Today's networks make available avariety of KPIs that reflect network performance, many focused onspecific layer(s) of the OSI model shown in FIG. 21. To better improvelink conditioning, the subject disclosure introduces a new set of KPIsthat can provide a more complete “spectral portrait” that describes theRF environment that the RF link depends upon.

There can be several aspects of the RF spectrum that can impact an RFlink, as shown in FIG. 24. For example, one aspect of the RF spectrumthat can impact the RF link involves the condition of a particularfrequency band used for a desired signal. Other co-channel signals inthe same frequency band can have an impact on the RF link, whether dueto inter-cell interference from neighboring cell sites or externalforeign interference from faulty systems and unintentional radiators.Each desired frequency band also has neighbors ranging from guard bandsleft open to provide isolation, additional carriers used by the samewireless operator (e.g., multiple UMTS bands or LTE neighboring CDMA),competing carriers operating in near adjacent bands, other systemsoperating in adjacent bands, and so on.

Each of four different RF categories measured during link conditioning(enumerated as 1-4 in FIG. 24) can provide important RF information thatcan directly impact a condition of the RF link and ultimately the UE—eNBrelationship. Link conditioning as described by the subject disclosureprovides a holistic spectral portrait enabling more insight than what isprovided by OEM (Original Equipment Manufacturer) equipment whichcollects RSSI information and carrier power information in band (e.g.,only 1 of the 4 groups), but doesn't give an operator visibility intowhat is happening in adjacent bands, out of band, or unused spectrum.Prior art OEM equipment also does not provide a comparison betweenexpected, averages and daily measurements, which if available wouldprovide a service provider a way to measure network performance.

Co-channel signals in an operating band can be filtered using thefiltering techniques described earlier in the subject disclosure. FIG.25 describes the four categories of bands in each of current USspectrums. In some instances these classes of RF segments are presentlyimpacting the performance of the underlying RF link and therefore theoverall network performance. To support active conditioning of an RFlink, the new KPIs introduced above along with SINR monitoring canprovide visibility to parameters not currently available, and can beused to mitigate spectrum and link conditions that may be undesirable.Such parameters can include absolute nominal values for each RFtechnology such as, for example, SINR targets based on nominal valuesand site-specific values based on particular conditions of a cell site.For example, some sites can have a target SINR higher than others due tothe nature of traffic the sites support and/or because of network designconsiderations.

A network is a dynamic entity that changes continuously due to softwareupgrades, traffic volumes and pattern changes, seasonality andenvironmental conditions, just to name a few. Monitoring thesevariations and then adjusting the RF link to accurately compensate forsuch variations enables cell sites to consistently operate with adesired performance. In addition to monitoring and adjusting variationsin an RF link, in one embodiment, nominal spectral values and RFstatistics can be recorded in an ongoing basis (daily, hourly, accordingto moving averages, etc.).

Occasionally there can be significant differences between real-time,short-term averages and longer-term design parameters that can causedegradation of cell site metrics, which may negatively impact customerexperience, and which can result in lost revenue for a service providerif not counteracted. When such issues are identified a next step can beto understand why the issues arose by analyzing spectral insights gainedthrough the analysis of signals impacting SINR.

In one embodiment, link conditioning can be performed based on a numberof metrics that can include without limitation:

-   -   RSSI_(OUT)—RSSI in the neighboring frequency bands (out of        band). For example, TV channel 51 adjacent to the lower 700 MHz        LTE bands or SMR and public safety bands adjacent to the 800 MHz        cellular bands. This metric is proportional to N_(out).    -   RSSI_(CB) and RSSI_(CM)—RSSI per carrier during busy hour and        during maintenance window which can be used to help estimate S.    -   RSSI_(c)—RSSI in carrier's used spectrum. This metric is        proportional to S+N_(c).    -   RSSI_(ADJ)—RSSI in band in the carrier's unused spectrum. This        metric is proportional to N_(adj).    -   RSSI_(COMP)—RSSI of competing wireless carriers occupying        adjacent spectrum, not filtered by front end. This metric is        proportional to N_(comp).    -   SINR—the signal to noise plus interference ratio of the resource        blocks.

To gain a better understanding of the above metrics, the referencenumbers 1-4 used in the above listing can be cross-referenced with thereference numbers 1-4 in FIGS. 24-25. These metrics can be measured on apath-by-path basis and can be used to drive optimization of one or morecell sites. As the environment changes, so can the performance of anetwork which can be reflected in these metrics. Using these metrics andcorrelating them against spectral KPIs can reveal vital information thatcan be used to improve an RF link's performance.

Analysis.

As variations of RSSI and SINR data are collected RF statistics relatingto these metrics can be generated and used to mine a data set fortrends, outliers, and abnormalities across cell sites and frequencybands. By analyzing such information, a network and its correspondingcell sites can be monitored for changes over time, and correspondingmitigation steps can be taken when necessary.

Recall equation EQ1 above,

${SINR} = {\frac{Signal}{{Interference} + {Noise}} = \frac{S}{N + N_{c} + N_{adj} + N_{comp} + N_{out} + {\Sigma \; I}}}$

where S is the received signal level, N is the thermal noise, and N_(c)is in-band co-channel interference, N_(adj) is the adjacent band noise,N_(comp) is interference from other operators, N_(out) is theout-of-band noise, and ΣI is the summation of the inter-cellinterference contributed from all the surrounding cells. If SINR of agiven sector or node is lower than expected a number of causes andsolutions can be applied, based on a deeper understanding of the RFenvironment and its contribution to SINR. Below are non-limitingillustrations and corresponding recommended solutions to improve SINR.

-   1. N_(out) is high, the solution may be to provide better filtering    or diversity optimization-   2. N_(comp) is high, the solution may be to incorporate dynamic    filtering to eliminate those sources-   3. N_(adj) is high, the solution may be to incorporate dynamic    filtering to eliminate those sources or 3G service optimization    (e.g., pilot power reduction or antenna tilt)-   4. N_(c) is high, the solution may in band mitigation using    filtering techniques described in the subject disclosure-   5. ΣI is high, the solution may involve reducing overall gain to    minimize intra-cell site noise-   6. S is low, the solution may be to increase uplink gain to improve    the RF link of the UE

The above listing provides illustrations for initiating mitigatingactions based on spectral analysis, which can be implemented with closedloop control so that ongoing performance improvements can be maintained.

Mitigation (Do).

RF link mitigation can be initiated from an analysis of spectral datathat leads to a set of specific recommended actions. There are manyaspects of the RF link that can be modified as part of a link mitigationstrategy, including without limitation:

-   -   Filtering adjacent signals: If adjacent signals are detected at        the eNodeB at higher levels than expected, antennas can be        tilted away from adjacent systems and/or digital filtering can        be applied to the uplink to provide additional adjacent channel        selectivity.    -   Adding gain: Based on traffic conditions or trends. For example,        cell sites can be directed to increase uplink gain, effectively        improving SINR for received signals or expanding coverage of a        cell site.    -   Attenuating high signal power: In situations involving high        traffic or locations of certain types of traffic leading to high        signal power in-band, base station transceivers (BTS) can be        instructed to reduce uplink signal power, which can improve an        eNodeB's operating performance    -   Interference suppression: in-band uplink filtering techniques        described in the subject disclosure can be used to remove        external interference within the carrier's active channel.    -   Diversity optimization: picking the better signal of a main and        diversity receive antennas.    -   3G service optimization: Adjusting 3G pilot power or 3G antennas        to minimize interference.    -   Adjusting mobile transmit parameters: Working with SON        interfaces and eNodeB to adjust target power levels to modify        cell coverage or reduce inter-cell interference.    -   Tilting antennas to reshape coverage: As traffic moves and        capacity demand shifts, providing control of antenna tilt or        input to antenna SON algorithms can enable the network to adjust        coverage to address traffic demands. Coordinating across        multiple sites, link conditioning algorithms can adjust        positions of antennas (e.g., tilt down) on one site to reduce        coverage and focus capacity while simultaneously up-tilting        antennas of neighboring sites to fill in coverage gaps. This can        shift traffic reducing the interference from UEs serviced by        neighboring sites.

Check and Reporting.

As changes are made to the network parameters based on any of themitigation actions described above, the changes can be verified todetermine whether such mitigation actions in fact improved networkperformance. Additionally, the SON network can be informed of thesechanges on the uplink for possible use in downlink conditioning aspreviously described. In addition, relevant data can be logged to guidefuture enhancement cycles.

As noted earlier, verification of the changes to the RF link can beimplemented by way of a closed loop confirmation process which canprovide input to the SON network to ensure that the network as a wholeis operating according to up-to-date settings and the same or similar RFdata. Reports generated in the verification step may include informationrelating to external interference that was detected, resource blockutilization, multiple channel power measurements, etc.

As part of the ongoing adaptation of the link conditioning cycle, allchanges can be logged, statistics can be updated and metadata can begenerated and/or assigned to logged changes to ensure all changes can beunderstood and analyzed by personnel of a service provider. Such reportscan also be used by future applications which can be adapted to “learn”from historical data generated from many cycles of the process describedabove. Implementing a link conditioning process based on real-worldconditions as described above provides an enhanced and optimized RFphysical layer performance. Ongoing link conditioning also enablesoperators to rely less on designing cell sites to worst-case conditionsor anticipated network coverage.

The embodiments of the subject disclosure provide a unique focus on theRF physical layer according to a collective analysis of RF links acrossmultiple cell sites. These embodiments enable systems to extract insightfrom spectral information, historical trends and network loading, whilesimultaneously optimizing RF parameters of multiple sites with livenetwork traffic, thereby improving communications between eNodeBs andthe UEs.

FIG. 26A depicts non-limiting illustrative embodiments of a method 800for implementing link management in a communication system. In oneembodiment, method 800 can be performed by a centralized system 832 thatcoordinates SINR measurements and corrective actions between cell sitesas depicted in FIG. 26B. In an alternate embodiment, method 800 can beperformed independently by each cell site without regard to adverseeffects that may be caused by a particular cell site on neighboring cellsite(s) as depicted in FIG. 26C. In another alternate embodiment, method800 can be performed by each cell site, each communicating with one ormore neighboring cell sites to reduce adverse effects caused by aparticular cell site on neighboring cell site(s) as depicted in FIG.26D. The embodiments of FIGS. 26B-26D can be combined in any fashion inrelation to applications of method 800. For example, suppose method 800is implemented independently by cell sites depicted in FIG. 26C. Furthersuppose the centralized system 832 of FIG. 26B receives SINR resultsfrom each of the cell sites performing method 800. In this illustration,the centralized system 832 can be configured to reverse or modify some(or all) of the independent actions of the cell sites of FIG. 26Cdepending on SINR measurements received by the centralized system 832from the cell sites. Other combinations of FIGS. 26B-26D are possibleand should be considered in relation to method 800.

For illustration purposes only, method 800 will now be describedaccording to the centralized system 832 of FIG. 26B. Method 800 canbegin at step 802 where a SINR measurement can be made by each cell siteon a corresponding sector and/or path. Cell sites can be configured toperform SINR measurements over several iterations which can be averagedover time. Each cell site can share SINR measurements with thecentralized system 832. The SINR measurements can include a SINRmeasurement for the cell site, a SINR measurement for each sector, aSINR measurement for each path, or combinations thereof. The SINRmeasurement for a sector can be an average of SINR measurements for thepaths of the sector. The SINR measurement for the cell site can be anaverage of SINRs measurements of multiple sectors, or SINRs measurementsof multiple paths. When SINR measurements have been shared by all cellsites, a determination can be made by the centralized system 832 at step804 as to which of the cell sites, sectors, or paths has the lowest SINRmeasurement. The minimum SINR measurement can then be compared by thecentralized system 832 in step 806 to one or more thresholds which maybe established by a service provider as a minimum expected SINRperformance for any particular cell site, sector, and/or path. If theminimum SINR measurement is not below the threshold, the centralizedsystem 832 can proceed to step 802 and reinitiate measurements of SINRacross multiple cell sites and corresponding sectors and/or paths.

If, however, the minimum SINR measurement of a particular cell site,sector or path is below the threshold, then corrective action can betaken by the centralized system 832 at step 808 to improve the SINRmeasurement of the cell site, sector or path in question. The correctiveaction can include, without limitation, filtering adjacent signals,adding gain, attenuating high signal power, filtering interferencesignals according to the embodiments of the subject disclosure,utilizing diversity optimization, utilizing 3G service optimization,adjusting mobile transmit parameters, tilting antennas to reshape cellsite coverage, or any combination thereof.

Once corrective action has been executed by a cell site and/or UE, adetermination can be made by the centralized system 832 at step 810 asto whether the SINR of the cell site, sector or path in question hasimproved. If there's no improvement, the corrective action can bereversed in whole or in part by the centralized system 832 at step 812,and measurements of SINR per cell site, sector and/or path can berepeated beginning from step 802. If, however, the corrective action didimprove the SINR of the cell site, sector or path in question, then adetermination can be made by the centralized system 832 at step 814 asto whether the corrective action implemented by the cell site and/or UEhas had an adverse effect on other paths or sectors of the same cellsite or neighboring cell sites.

In one embodiment, this determination can be made by the centralizedsystem 832 by requesting SINR measurements from all cell sites, sectors,and/or paths after the corrective action has been completed. Thecentralized system 832 can then be configured to determine an average ofthe SINR's for all the cell sites, sectors, and/or paths for which thecorrective action of step 808 was not applied. For ease of description,the cell site that initiated corrective action will be referred to asthe “corrected” cell site, while cell sites not participating in thecorrective action will be referred to as the “uncorrected” cell sites.

With this in mind, at step 816, the centralized system 832 can determinewhether the SINR averages from the uncorrected cell sites, sectors orpaths are the same or similar to the SINR averages of the uncorrectedcell sites, sectors, and/or paths prior to the corrective action. Ifthere's no adverse effect or a nominal adverse effect, then thecentralized system 832 can be configured to maintain the correctiveaction initiated by the corrected cell site, sector and/or path andproceed to step 802 to repeat the process previously described. If, onthe other hand, the average of the SINR's of the uncorrected cell sites,sectors or paths for which corrective action was not taken has beenreduced below the SINR averages of these sites, sectors or paths priorto the corrective action (or below the threshold at step 806 ordifferent threshold(s) established by the service provider), then thecorrective action initiated by the corrective cell site, sector or pathcan be reversed in whole or in part by the centralized system 832 atstep 812.

In another embodiment, step 816 can be implemented by establishing aminimum SINR values that are unique to each cell site, sector, and/orpath. If after the corrective action the SINR measurements of thecorrected cell site, sector and/or path has improved at step 810 and theSINR measurements of the uncorrected cell sites, sectors and/or pathsare above the unique SINR values established therefor, then thecorrective action can be maintained by the centralized system 832 andthe process can be reinitiated at step 802. If, on the other hand, theSINR measurement of the corrected cell site, sector or path has notimproved after the corrective action, or the SINR measurements of one ormore uncorrected cell sites, sectors, and/or paths are below the uniqueSINR values established therefor, then the corrective action taken canbe reversed in whole or in part by the centralized system 832 at step812.

Method 800 can be adapted to use different sampling rates for SINR,and/or different thresholds. The sampling rates and/or thresholds can betemporally dependent (e.g., time of day profiles—morning, afternoon,evening, late evening, early morning, etc.). SINR profiles can be usedto account for anomalous events (e.g., a sporting event, a convention,etc.) which may impact traffic conditions outside the norm of regulartraffic periods. Thresholds used by method 800 can include withoutlimitation—minimum thresholds used for analyzing SINRs of cell sites,sectors and/or paths prior to corrective action; corrective thresholdsused for analyzing SINRs of corrected cell sites, sectors and/or paths,consistency thresholds used for analyzing SINRs from uncorrected cellsites, sectors and/or paths after corrective action, and so on. Method800 can also be adapted to use other KPIs such as dropped calls, datathroughput, data rate, accessibility and retain-ability, RSSI, densityof user equipment (UEs), etc. Method 800 can also be adapted to ignoreor exclude control channels when determining SINR measurements. Thatpower levels from control channels can be excluded from SINRmeasurements. Method 800 can also be adapted to perform closed-loopmethods for balancing uplink and downlink performance as describedearlier for SON networks, cell sites, UEs, or combinations thereof.Method 800 can be adapted to obtain the noise components of SINR (EQ1)from power measurements described in the subject disclosure. Referringto FIG. 24, the RSSI measurements shown in FIG. 24 can be determined bymeasuring power levels at different spectral locations in the spectralcomponents shown in FIG. 24.

As noted earlier, method 800 can also be adapted to the architectures ofFIGS. 26C and 26D. For example, method 800 can be adapted for use byeach cell site of FIG. 26C. In this embodiment, each cell site canindependently perform SINR measurements per sector and/or path, performanalysis based on expected SINR threshold(s), mitigate below performanceSINRs, verify corrective actions, and reverse when necessary correctivemeasures in whole or in part as described earlier. A distinct differencebetween this embodiment and that described for the centralized system832 of FIG. 26B is that in this embodiment, each cell site can takecorrective action without regard to adverse effects that may be causedto neighboring cell site(s) shown in FIG. 26C.

In the case of FIG. 26D, method 800 can adapted for use by each cellsite with the additional feature that each cell site can be adapted tocooperate with its neighboring cell sites to avoid as much as possibleadverse effects caused by corrective actions taken by any of the cellsites. In this embodiment, a corrected cell site can request SINRmeasurements of neighboring (uncorrected) cell sites, sectors or pathsfrom the uncorrected cell sites themselves or a centralized systemmonitoring SINR measurements. Such requests can be made before or aftercorrection action is performed by the corrected cell site. For example,before corrective action is taken, a cell site that needs correction candetermine whether the SINR measurements of one or more neighboring cellsites, sectors or paths are marginal, average or above average whencompared to expected SINR performance threshold(s). The cell site to becorrected can use this information to determine how aggressive it can bewhen initiating corrective action. After corrective action is taken, thecorrected cell site can request updated SINR measurements fromneighboring cell sites which it can then compare to threshold(s)established for the neighboring cell sites and determine therefromwhether to reverse the corrective action in whole or in part.

It is further noted that method 800 can be adapted to combine one ormore of the foregoing embodiments for performing link conditioning inany one of the embodiments FIGS. 26B, 26C, and 26D such that combinedimplementations of method 800 are used to achieve a desirable RF linkperformance for clusters of cell sites in a network.

FIG. 27A depicts a non-limiting, illustrative embodiment of a method 900for determining an adaptive inter-cell interference threshold based onthermal noise measured from unused wireless signal paths. Wirelesssignal paths can be, for example, channels, communication channels,cellular connections, spectral segments, and/or radio channels. In oneor more embodiments, in step 904, the system and methods of the subjectdisclosure can be adapted to obtain one or more resource block (RB)schedules associated with one or more paths, one or more sectors, and/orone or more cell sites. For example, an LTE controller in a base station16 (such as depicted in FIG. 4) can assign to UE's data packet trafficto certain resource blocks. The term base station and cell site may beused interchangeably in the subject disclosure. The UE's can access theresource block scheduling information in a control channel. Inparticular, the resource block schedule can include informationregarding which of the scheduled resource blocks are used to carry dataand which are unused. In one embodiment, the systems and methods of thesubject disclosure can obtain from a transmit link (downlink) of thebase station 16 parametric information relating to the downlink (e.g., aresource block or RB schedule, gain being used on the downlink, tiltposition of the antenna, etc.). The downlink parametric information canbe obtain by demodulating the transmit link. In an embodiment, thesystems and methods of the subject disclosure can be adapted to obtainthe downlink parametric information without demodulation (e.g., from afunctional module of the base station). In step 908, the system andmethods of the subject disclosure can be adapted to identify the unusedresource block for certain wireless signal paths from the resource blockschedule. Since RB schedule(s) can be obtained for multiple paths,sectors and/or cell sites, the unused resource blocks can be associatedwith one or more cell sites, one or more sectors, and/or one or morepaths.

In one or more embodiments, in step 910, the system and methods of thesubject disclosure can be adapted to measure signal energy levels duringthe unused resource blocks. In one embodiment, the resource blocks thatare scheduled for use in carrying data information will bear RF signalsduring particular portions of frequency/time identified by the resourceblock schedule. By comparison, when the resource blocks are notscheduled to carry data information, the resource blocks should not bearRF signals during particular portions of frequency/time identified bythe resource block schedule.

Put another way, no active transmission power should be allocated to thetime-frequency signal space by a transmitting LTE UE during the unusedresource blocks, while active transmission power is expected to beallocated to the time-frequency signal space by a transmitting LTE UEduring “used” resource blocks. Therefore, during the unused resourceblocks, a wireless signal path should exhibit a lower energy level thanduring the “in use” resource blocks. In one embodiment, signal energylevels in unused resource blocks can be measured for one or morewireless signal paths, one or more sectors, or one or more sectors.Generally, the energy measured in the unused resource blocks should bethermal noise only. However, if inter-cell (i.e., adjacent cell site)interference is present, the energy measured in one or more unusedresource blocks may be above an expected thermal noise level.

In one or more embodiments, in step 912 the system and methods of thesubject disclosure can be adapted to determine an average thermal noiselevel from the measured signal energy levels of the wireless signalpath(s) during unused resource blocks. In one embodiment, the averagesystem noise can be determined per resource block of a particular path.In one embodiment, the average system noise can be determined across allunused resource blocks of the particular path. In one embodiment, theaverage system noise can be determined across a subset of unusedresource blocks of the particular path. In one embodiment, the averagesystem noise can be determined for unused resource blocks across allpaths of a particular sector. In one embodiment, the average systemnoise can be determined for unused resource blocks across multiplesectors. In one embodiment, the average system noise can be determinedfor unused resource blocks across multiple cell sites. Based on theforegoing illustrations, any combination of averaging of energymeasurements across one or more unused resource blocks is possible fordetermining an average thermal noise at step 912.

A sample of unused resource blocks can be selected so that measurementsand thermal noise averaging is distributed over a time period or can beselected based on another organized factor. For example, the sample ofunused resource blocks could be based on the relative traffic loading,where greater or lesser numbers of unused resource blocks could beselected for measurement and thermal noise averaging can be based on thedata traffic load. In another example, the sample of unused resourceblocks could be selected for measurement and thermal noise averagingbased on changes in noise and/or error rate conditions for the wirelesssignal paths. Noise and/or error rate conditions can be monitored andclassified as improving, deteriorating, and/or steady state. Underimproving or steady state noise/error rate trends, a reduced set ofunused resource blocks can be selected for measurement of signal energylevels and thermal noise averaging determined therefrom, while a largerset of unused resource blocks can be selected under deterioratingnoise/error rate conditions. The sample of unused resource blocksselected can also depend on known or scheduled events, time of day, dayof the week, or combinations thereof. For example, traffic conditionsmay vary during time of day. Thus traffic conditions can be profileddaily and by geography (e.g., heavy traffic from noon to late afternoon,lighter traffic at other times). Events such as sporting events orconventions can also change traffic conditions.

Accordingly, the system and methods of the subject disclosure canmeasure signals levels and determine thermal noise averages based on asample of unused resource blocks selected according to any number oftechniques including without limitation distributing measured energylevels of unused resource blocks over a time period, accounting fortraffic conditions at different times of the day, scheduled events thatcan change traffic conditions, and/or responding to trends innoise/error rate levels. In another embodiment, the measured energylevels can be weighted to emphasize or deemphasize certain measurementsbased on criteria such as location of wireless signal paths, relativeimportance of wireless signal paths, and/or how recently the measurementwas collected. The average thermal noise level can be determine from theweighted sample of measured energy levels. In one embodiment, theaverage thermal noise level can be adjusted. Additionally, certainmeasured energy levels can be excluded from a thermal noise averagingcalculation (e.g., excluding unexpectedly high measured energy levels incertain unused resource blocks, excluding measured energy levels thatexceed a threshold, and so on).

In one or more embodiments, in step 916 the system and methods of thesubject disclosure can be adapted to determine an adaptive inter-cellinterference threshold for one or more wireless signal paths, one ormore sectors, and/or one or more cell cites based on the average thermalnoise level determined at step 912. In one embodiment, the adaptiveinter-cell interference threshold can be based on the average thermalnoise level determined at step 912 with no additional factors. Inanother embodiment, the adaptive inter-cell interference threshold canbe determined from a sum of a threshold supplied by a service providerand the average noise level determined at step 912.

In one or more embodiments, in step 920 the system and methods of thesubject disclosure can be adapted to scan signals in one or morewireless paths. The scanned signals can represent signals measured inone or more resource blocks of one or more wireless paths. If a resourceblock schedule is available to identify which resource blocks are in useas in the present case, then the scanned signals can represent signalsmeasured in one or more “used” resource blocks. Alternatively, inanother embodiment, whether or not a resource block schedule isavailable, the scanned signals can represent signals measured in one ormore resource blocks that may include used and unused resource blocks.In one or more embodiments, the scanned signals can be compared to theadaptive inter-cell interference threshold at step 924 to detectinterference signals that may adversely affect communications betweenUEs and base station(s). If the scanned signals exceed the adaptiveinter-cell interference threshold in step 924, then the interferencesignal energies and frequencies are stored, in step 928.

In one or more embodiments, in step 932 the system and methods of thesubject disclosure can be adapted to measure signal energy levels andnoise energy levels of wireless signal paths. Again, the wireless signalpaths can be selected for measurement of signal levels and noise levelsbased on one or more criteria, such as changes in noise or error ratetrends, criticality of one or more wireless signal paths, loading of oneor more wireless signal paths, availability of system resources, amongother possible factors. The measured noise levels can include, forexample, the noise factors previously described for EQ1, e.g., in-bandco-channel interference (N_(c)), adjacent band noise in guard bands orthe operator's other carriers (N_(adj)), N_(comp) interference in thesame overall frequency band from other operators, and N_(out)out-of-band noise. Thermal noise (N) in the present case can be based onthe average thermal noise determined at step 912.

In one or more embodiments, in step 936 the system and methods of thesubject disclosure can be adapted to determine signal-to-interferenceplus noise ratios (SINR) for the wireless signal paths based on measuredsignal levels, noise levels, and interference levels. The SINR can bedetermined by processing, in a SINR model, the measured signal and noiseenergy levels from step 932 and the stored above-threshold interferencesignal energy levels from step 928. The SINR model can be the equationfor SINR calculation described above (EQ1). In one embodiment, SINRvalues can be determined for all wireless signal paths or for selectedwireless signal paths. The wireless signal paths can be selected basedon one or more criteria, such as changes in noise or error rate trends,criticality of one or more wireless signal paths, loading of one or morewireless signal paths, and/or availability of system resources. In oneembodiment, SINR values can be generated and reported on a periodicbasis. In one embodiment, SINR values can be generated and reportedresponsive to a system encountering communication issues between UEs andone or more cell sites.

In one or more embodiments, in step 940 the system and methods of thesubject disclosure can be adapted to compare SINR values to one or moreSINR thresholds to determine if any of the wireless signal paths isexhibiting an SINR value that is below the SINR threshold. If the awireless signal path is operating with a below-threshold SINR, then, instep 944 the system and methods of the subject disclosure can be adaptedto initiate a corrective action to improve the SINR for the wirelesssignal paths, as described above in relation to FIGS. 26A-26D.

FIG. 27B depicts an illustrative embodiment of another method 950 fordetermining an adaptive inter-cell interference threshold based on anestimated thermal noise energy. In situations where resource blockschedule(s) cannot be obtained, method 950 replaces block 902 of FIG.27A with block 952 of FIG. 27B. In one or more embodiments, in step 954,the system and methods of the subject disclosure can be adapted todetermine an estimated thermal noise energy levels at wireless signalpaths of the system. In one embodiment, the thermal noise energy levelscan be estimated for one or more wireless signal paths, one or moresectors, or one or more cell sites. In one embodiment, all wirelesssignal paths of the system can be associated with the same estimatedthermal noise energy. In one embodiment, different wireless signal pathscan be associated with different estimated thermal noise levels based onone or more criteria, such as location of the wireless signal pathand/or noise/error rate trends for the wireless signal path. In oneembodiment, the estimated thermal noise level can be determined byidentifying resource blocks with the lowest signal levels or closest toan expected thermal noise level. In an embodiment, the system andmethods of the subject disclosure can be adapted to use all measuredsignal levels or a subset of such measurements. In an embodiment, thesystem and methods of the subject disclosure can be adapted to averageall measured signal levels or a subset of such measurements to estimatethe thermal noise. In an alternative embodiment, a default estimatedthermal noise level can be obtained from a service provider. The defaultthermal noise level can be modified to account for one or more criteria,such as the location of the wireless signal path.

In one or more embodiments, in step 958 the system and methods of thesubject disclosure can be adapted to adjust the estimated thermal noiseenergy according to a thermal noise threshold adjustment to create anadjusted estimated thermal noise energy. In one embodiment, the thermalnoise threshold adjustment can be provided by a service provider. In oneembodiment, the thermal noise threshold adjustment can be specific candifferent between wireless signal paths, sectors or cell sites. In oneembodiment, all wireless signal paths of the system can be associatedwith the same thermal noise threshold adjustment. In one embodiment, thethermal noise threshold adjustment can be determined by the system ordevice by accessing a configuration that is specific to a wirelesssignal path. In one embodiment, a default thermal noise thresholdadjustment can be used. The default thermal noise threshold adjustmentcan be modified to account for one or more criteria, such as thelocation of the wireless signal path or current noise/error trendinformation.

In one or more embodiments, in step 966 the system and methods of thesubject disclosure can be adapted to determine an average thermal noiselevel for the wireless signal paths, sectors, or cell sites based onaverages of the adjusted estimated thermal noise. In one embodiment, theaverage thermal noise level for the wireless signal paths, sectors orcell sites can be based on averaging of the unadjusted estimated thermalnoise of step 954. Steps 916-944 can be performed as described above inmethod 900.

It is further noted that the methods and systems of the subjectdisclosure can be used in whole or in part by a cellular base station(e.g., macro cell site, micro cell site, pico cell site, a femto cellsite), a wireless access point (e.g., a WiFi device), a mobilecommunication device (e.g., a cellular phone, a laptop, a tablet, etc.),a commercial or utility communication device such as amachine-to-machine communications device (e.g., a vending machine with acommunication device integrated therein, an automobile with anintegrated communication device), a meter for measuring powerconsumption having an integrated communication device, and so on.Additionally, such devices can be adapted according to the embodimentsof the subject disclosure to communicate with each other and shareparametric data with each other to perform in whole or in part any ofembodiments of the subject disclosure.

It is further noted that the methods and systems of the subjectdisclosure can be adapted to receive, process, and/or deliverinformation between devices wirelessly or by a tethered interface. Forexample, SINR information can be provided by the cell sites to a systemby way of a tethered interface such as an optical communication linkconforming to a standard such as a common public radio interface (CPRI)referred to herein as a CPRI link. In another embodiment, a CPRI linkcan be used to receive digital signals from an antenna system of thebase station for processing according to the embodiments of the subjectdisclosure. The processed digital signals can in turn be delivered toother devices of the subject disclosure over a CPRI link. Similaradaptations can be used by any of the embodiments of the subjectdisclosure.

Although reference has been made to resource blocks in the methods andsystems of the subject disclosure, the methods and systems of thesubject disclosure can be adapted for use with any spectral segment ofany size in the frequency domain, and any frequency of occurrence of thespectral segment in the time domain. Additionally, the methods andsystems of the subject disclosure can be adapted for use with adjacentspectral segments in the frequency domain, spectral segments separatedfrom each other in the frequency domain, and/or spectral segments ofdifferent wireless signal paths, sectors, or cell sites. It is furthernoted that the methods and systems of the subject disclosure can beperformed by a cell site operating independently of the performance ofother cell sites, by a cell site operating in cooperation with otheradjacent cell sites, and/or by a central system controlling operationsof multiple cell sites.

An illustrative embodiment of a communication device 1000 is shown inFIG. 28. Communication device 1000 can serve in whole or in part as anillustrative embodiment of the devices depicted in FIGS. 1, 4, and 6-8.In one embodiment, the communication device 1000 can be configured, forexample, to perform operations such as measuring a power level in atleast a portion of a plurality of resource blocks occurring in a radiofrequency spectrum, where the measuring occurs for a plurality of timecycles to generate a plurality of power level measurements, calculatinga baseline power level according to at least a portion of the pluralityof power levels, determining a threshold from the baseline power level,and monitoring at least a portion of the plurality of resource blocksfor signal interference according to the threshold. Other embodimentsdescribed in the subject disclosure can be used by the communicationdevice 1000.

To enable these features, communication device 1000 can comprise awireline and/or wireless transceiver 1002 (herein transceiver 1002), auser interface (UI) 1004, a power supply 1014, a location receiver 1016,a motion sensor 1018, an orientation sensor 1020, and a controller 1006for managing operations thereof. The transceiver 1002 can supportshort-range or long-range wireless access technologies such asBluetooth, ZigBee, WiFi, DECT, or cellular communication technologies,just to mention a few. Cellular technologies can include, for example,CDMA-1X, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO, WiMAX, SDR, LTE, aswell as other next generation wireless communication technologies asthey arise. The transceiver 1002 can also be adapted to supportcircuit-switched wireline access technologies (such as PSTN),packet-switched wireline access technologies (such as TCP/IP, VoIP,etc.), and combinations thereof.

The UI 1004 can include a depressible or touch-sensitive keypad 1008with a navigation mechanism such as a roller ball, a joystick, a mouse,or a navigation disk for manipulating operations of the communicationdevice 1000. The keypad 1008 can be an integral part of a housingassembly of the communication device 1000 or an independent deviceoperably coupled thereto by a tethered wireline interface (such as a USBcable) or a wireless interface supporting for example Bluetooth. Thekeypad 1008 can represent a numeric keypad commonly used by phones,and/or a QWERTY keypad with alphanumeric keys. The UI 1004 can furtherinclude a display 1010 such as monochrome or color LCD (Liquid CrystalDisplay), OLED (Organic Light Emitting Diode) or other suitable displaytechnology for conveying images to an end user of the communicationdevice 1000. In an embodiment where the display 1010 is touch-sensitive,a portion or all of the keypad 1008 can be presented by way of thedisplay 1010 with navigation features.

The display 1010 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 1000 can be adapted to present a user interfacewith graphical user interface (GUI) elements that can be selected by auser with a touch of a finger. The touch screen display 1010 can beequipped with capacitive, resistive or other forms of sensing technologyto detect how much surface area of a user's finger has been placed on aportion of the touch screen display. This sensing information can beused to control the manipulation of the GUI elements or other functionsof the user interface. The display 1010 can be an integral part of thehousing assembly of the communication device 1000 or an independentdevice communicatively coupled thereto by a tethered wireline interface(such as a cable) or a wireless interface.

The UI 1004 can also include an audio system 1012 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high volume audio (such as speakerphonefor hands free operation). The audio system 1012 can further include amicrophone for receiving audible signals of an end user. The audiosystem 1012 can also be used for voice recognition applications. The UI1004 can further include an image sensor 1013 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 1014 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 1000 to facilitatelong-range or short-range portable applications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 1016 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 1000 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor1018 can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 1000 in three-dimensional space. Theorientation sensor 1020 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device1000 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 1000 can use the transceiver 1002 to alsodetermine a proximity to a cellular, WiFi, Bluetooth, or other wirelessaccess points by sensing techniques such as utilizing a received signalstrength indicator (RSSI) and/or signal time of arrival (TOA) or time offlight (TOF) measurements. The controller 1006 can utilize computingtechnologies such as a microprocessor, a digital signal processor (DSP),programmable gate arrays, application specific integrated circuits,and/or a video processor with associated storage memory such as Flash,ROM, RAM, SRAM, DRAM or other storage technologies for executingcomputer instructions, controlling, and processing data supplied by theaforementioned components of the communication device 400.

Other components not shown in FIG. 27 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 1000 can include a reset button (not shown). The reset button canbe used to reset the controller 1006 of the communication device 1000.In yet another embodiment, the communication device 1000 can alsoinclude a factory default setting button positioned, for example, belowa small hole in a housing assembly of the communication device 1000 toforce the communication device 1000 to re-establish factory settings. Inthis embodiment, a user can use a protruding object such as a pen orpaper clip tip to reach into the hole and depress the default settingbutton. The communication device 1000 can also include a slot for addingor removing an identity module such as a Subscriber Identity Module(SIM) card. SIM cards can be used for identifying subscriber services,executing programs, storing subscriber data, and so forth.

The communication device 1000 as described herein can operate with moreor less of the circuit components shown in FIG. 28. These variantembodiments can be used in one or more embodiments of the subjectdisclosure.

It should be understood that devices described in the exemplaryembodiments can be in communication with each other via various wirelessand/or wired methodologies. The methodologies can be links that aredescribed as coupled, connected and so forth, which can includeunidirectional and/or bidirectional communication over wireless pathsand/or wired paths that utilize one or more of various protocols ormethodologies, where the coupling and/or connection can be direct (e.g.,no intervening processing device) and/or indirect (e.g., an intermediaryprocessing device such as a router).

FIG. 29 depicts an exemplary diagrammatic representation of a machine inthe form of a computer system 1100 within which a set of instructions,when executed, may cause the machine to perform any one or more of themethods described above. One or more instances of the machine canoperate, for example, as the devices of FIGS. 1, 4, and 6-8. In someembodiments, the machine may be connected (e.g., using a network 1126)to other machines. In a networked deployment, the machine may operate inthe capacity of a server or a client user machine in server-client usernetwork environment, or as a peer machine in a peer-to-peer (ordistributed) network environment.

The machine may comprise a server computer, a client user computer, apersonal computer (PC), a tablet PC, a smart phone, a laptop computer, adesktop computer, a control system, a network router, switch or bridge,or any machine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. It will beunderstood that a communication device of the subject disclosureincludes broadly any electronic device that provides voice, video ordata communication. Further, while a single machine is illustrated, theterm “machine” shall also be taken to include any collection of machinesthat individually or jointly execute a set (or multiple sets) ofinstructions to perform any one or more of the methods discussed herein.

The computer system 1100 may include a processor (or controller) 1102(e.g., a central processing unit (CPU), a graphics processing unit (GPU,or both), a main memory 1104 and a static memory 1106, which communicatewith each other via a bus 1108. The computer system 1100 may furtherinclude a display unit 1110 (e.g., a liquid crystal display (LCD), aflat panel, or a solid state display. The computer system 1100 mayinclude an input device 1112 (e.g., a keyboard), a cursor control device1114 (e.g., a mouse), a disk drive unit 1116, a signal generation device1118 (e.g., a speaker or remote control) and a network interface device1120. In distributed environments, the embodiments described in thesubject disclosure can be adapted to utilize multiple display units 1110controlled by two or more computer systems 1100. In this configuration,presentations described by the subject disclosure may in part be shownin a first of the display units 1110, while the remaining portion ispresented in a second of the display units 1110.

The disk drive unit 1116 may include a tangible computer-readablestorage medium 1122 on which is stored one or more sets of instructions(e.g., software 1124) embodying any one or more of the methods orfunctions described herein, including those methods illustrated above.The instructions 1124 may also reside, completely or at least partially,within the main memory 1104, the static memory 1106, and/or within theprocessor 1102 during execution thereof by the computer system 1100. Themain memory 1104 and the processor 1102 also may constitute tangiblecomputer-readable storage media.

Dedicated hardware implementations including, but not limited to,application specific integrated circuits, programmable logic arrays andother hardware devices that can likewise be constructed to implement themethods described herein. Application specific integrated circuits andprogrammable logic array can use downloadable instructions for executingstate machines and/or circuit configurations to implement embodiments ofthe subject disclosure. Applications that may include the apparatus andsystems of various embodiments broadly include a variety of electronicand computer systems. Some embodiments implement functions in two ormore specific interconnected hardware modules or devices with relatedcontrol and data signals communicated between and through the modules,or as portions of an application-specific integrated circuit. Thus, theexample system is applicable to software, firmware, and hardwareimplementations.

In accordance with various embodiments of the subject disclosure, theoperations or methods described herein are intended for operation assoftware programs or instructions running on or executed by a computerprocessor or other computing device, and which may include other formsof instructions manifested as a state machine implemented with logiccomponents in an application specific integrated circuit or fieldprogrammable gate array. Furthermore, software implementations (e.g.,software programs, instructions, etc.) including, but not limited to,distributed processing or component/object distributed processing,parallel processing, or virtual machine processing can also beconstructed to implement the methods described herein. It is furthernoted that a computing device such as a processor, a controller, a statemachine or other suitable device for executing instructions to performoperations or methods may perform such operations directly or indirectlyby way of one or more intermediate devices directed by the computingdevice.

While the tangible computer-readable storage medium 622 is shown in anexample embodiment to be a single medium, the term “tangiblecomputer-readable storage medium” should be taken to include a singlemedium or multiple media (e.g., a centralized or distributed database,and/or associated caches and servers) that store the one or more sets ofinstructions. The term “tangible computer-readable storage medium” shallalso be taken to include any non-transitory medium that is capable ofstoring or encoding a set of instructions for execution by the machineand that cause the machine to perform any one or more of the methods ofthe subject disclosure.

The term “tangible computer-readable storage medium” shall accordinglybe taken to include, but not be limited to: solid-state memories such asa memory card or other package that houses one or more read-only(non-volatile) memories, random access memories, or other re-writable(volatile) memories, a magneto-optical or optical medium such as a diskor tape, or other tangible media which can be used to store information.Accordingly, the disclosure is considered to include any one or more ofa tangible computer-readable storage medium, as listed herein andincluding art-recognized equivalents and successor media, in which thesoftware implementations herein are stored.

Although the present specification describes components and functionsimplemented in the embodiments with reference to particular standardsand protocols, the disclosure is not limited to such standards andprotocols. Each of the standards for Internet and other packet switchednetwork transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP) representexamples of the state of the art. Such standards are from time-to-timesuperseded by faster or more efficient equivalents having essentiallythe same functions. Wireless standards for device detection (e.g.,RFID), short-range communications (e.g., Bluetooth, WiFi, Zigbee), andlong-range communications (e.g., WiMAX, GSM, CDMA, LTE) can be used bycomputer system 1100.

The illustrations of embodiments described herein are intended toprovide a general understanding of the structure of various embodiments,and they are not intended to serve as a complete description of all theelements and features of apparatus and systems that might make use ofthe structures described herein. Many other embodiments will be apparentto those of skill in the art upon reviewing the above description. Theexemplary embodiments can include combinations of features and/or stepsfrom multiple embodiments. Other embodiments may be utilized and derivedtherefrom, such that structural and logical substitutions and changesmay be made without departing from the scope of this disclosure. Figuresare also merely representational and may not be drawn to scale. Certainproportions thereof may be exaggerated, while others may be minimized.Accordingly, the specification and drawings are to be regarded in anillustrative rather than a restrictive sense.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement calculated toachieve the same purpose may be substituted for the specific embodimentsshown. This disclosure is intended to cover any and all adaptations orvariations of various embodiments. Combinations of the aboveembodiments, and other embodiments not specifically described herein,can be used in the subject disclosure.

The Abstract of the Disclosure is provided with the understanding thatit will not be used to interpret or limit the scope or meaning of theclaims. In addition, in the foregoing Detailed Description, it can beseen that various features are grouped together in a single embodimentfor the purpose of streamlining the disclosure. This method ofdisclosure is not to be interpreted as reflecting an intention that theclaimed embodiments require more features than are expressly recited ineach claim. Rather, as the following claims reflect, inventive subjectmatter lies in less than all features of a single disclosed embodiment.Thus the following claims are hereby incorporated into the DetailedDescription, with each claim standing on its own as a separately claimedsubject matter.

What is claimed is:
 1. A cell site, comprising: a memory that storesinstructions; and a processor coupled to the memory, wherein responsiveto executing the instructions the processor performs operationscomprising: measuring noise levels for a plurality of paths; determiningan adaptive inter-cell interference threshold, wherein the adaptiveinter-cell interference threshold comprises determining an averagethermal noise level from the noise levels measured for unused paths inthe plurality of paths; determining Signal to Interference plus NoiseRatio (SINR) measurements for each of the plurality of paths accordingto signals, noise levels and interference signals for each of theplurality of paths; identifying a SINR measurement from the SINRmeasurements that is below a SINR threshold; and initiating a correctiveaction to improve the SINR measurement of an affected path of theplurality of paths falling below the SINR threshold.
 2. The cell site ofclaim 1, wherein the determining the adaptive inter-cell interferencethreshold comprises determining an average of the noise levels measuredof the unused paths in the plurality of paths and summing the averagewith a threshold value.
 3. The cell site of claim 2, wherein thedetermining the average comprises: measuring a thermal noise level ofeach of a plurality of unused channels; and averaging the thermal noiselevel of the plurality of unused channels to generate the average. 4.The cell site of claim 3, wherein the plurality of unused channelscomprise a plurality of unused spectral segments.
 5. The cell site ofclaim 4, wherein the plurality of unused spectral segments comprise aplurality of unused resource blocks.
 6. The cell site of claim 2,wherein the determining the average comprises obtaining a schedule forreceiving the signals in spectral segments from communication devices.7. The cell site of claim 6, wherein the determining the average furthercomprises identifying unused spectral segments from the schedule andmeasuring a thermal noise level of each of the unused spectral segmentsto determine the average.
 8. The cell site of claim 2, wherein theaverage is determined for each of a plurality of timeslots associatedwith spectral segments used by communication devices to transmit thesignals to the cell site.
 9. A non-transitory machine-readable storagemedium, comprising instructions, which when executed by a processor of acell site, the processor performs operations comprising: determining foreach of a plurality of paths an adaptive inter-cell interferencethreshold, wherein the adaptive inter-cell interference thresholdcomprises determining an average thermal noise level from noise levelsmeasured for each of the plurality of paths when each path is unused;measuring signals and noise levels for each of the plurality of paths;determining Signal to Interference plus Noise Ratio (SINR) measurementsfor the plurality of paths according to the signals, the noise levelsand interference signals for the plurality of paths; and identifying aSINR measurement that is below a SINR threshold; and initiating acorrective action to improve the SINR measurement of an affected path ofthe plurality of paths falling below the SINR threshold.
 10. Thenon-transitory machine-readable storage medium of claim 9, wherein theoperations further comprise: obtaining a resource block schedule foreach of the plurality of paths; identifying resource blocks in theplurality of paths that are not in use; and measuring for each of theplurality of paths an energy of the resource blocks not in use todetermine an average thermal noise level for each path, wherein theadaptive inter-cell interference threshold is determined from theaverage thermal noise level.
 11. A method comprising: determining, by asystem comprising a processor, an adaptive inter-cell interferencethreshold, wherein the adaptive inter-cell interference thresholdcomprises determining an average thermal noise level from noise levelsfor unused paths in a plurality of paths; determining, by the system,Signal to Interference plus Noise Ratio (SINR) measurements for each ofthe plurality of paths according to signals, noise levels andinterference signals for each of the plurality of paths; identifying, bythe system, a SINR measurement from the SINR measurements that is belowa SINR threshold; and initiating, by the system, a corrective action toimprove the SINR measurement of an affected path of the plurality ofpaths falling below the SINR threshold.
 12. The method of claim 11,further comprising: measuring a thermal noise level of each of theunused paths in the plurality of paths; and averaging the measuredthermal noise levels of each of the unused paths to generate the averagethermal noise level.
 13. The method of claim 12, further comprisingobtaining information identifying the unused paths in the plurality ofpaths.
 14. The method of claim 11, wherein the initiating of thecorrective action comprises improving the SINR measurement for anaffected network element that is below the SINR threshold.
 15. Themethod of claim 14, wherein the affected network element comprises anaffected cell site, and wherein the SINR measurements are obtained froma plurality of cell sites including the affected cell site.
 16. Themethod of claim 14, further comprising obtaining an updated SINRmeasurement from a neighboring network element in proximity to theaffected network element.
 17. The method of claim 11, wherein the SINRmeasurement is obtained for an uplink communication path.
 18. The methodof claim 11, wherein the SINR measurement is obtained for a downlinkcommunication path.
 19. The method of claim 11, wherein the initiatingof the corrective action comprises improving the SINR measurement for anaffected network element that is below the SINR threshold, and whereinthe system comprises a base station, a wireless access point, or awireless mobile communication device.
 20. The method of claim 19,further comprising: obtaining an updated SINR measurement from aneighboring network element in proximity to the affected networkelement; and reversing the corrective action at least in part responsiveto a comparison of the updated SINR measurement and the SINR measurementprior to the corrective action.